{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/haosulab/ManiSkill2/blob/main/examples/tutorials/2_reinforcement_learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1T7S6wmryEub"
      },
      "source": [
        "# Setup Code\n",
        "\n",
        "To begin, prepare the colab environment by clicking the play button below and make sure you are using a GPU runtime. This will install all dependencies for the future code. This can take up to 1.5 minutes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "kYugcL-gfwfz",
        "outputId": "3b2cf256-95fc-4e23-d1b8-f4502b6b3467",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libvulkan1\n",
            "Recommended packages:\n",
            "  mesa-vulkan-drivers | vulkan-icd\n",
            "The following NEW packages will be installed:\n",
            "  libvulkan-dev libvulkan1\n",
            "0 upgraded, 2 newly installed, 0 to remove and 16 not upgraded.\n",
            "Need to get 1,020 kB of archives.\n",
            "After this operation, 17.2 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 libvulkan1 amd64 1.3.204.1-2 [128 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 libvulkan-dev amd64 1.3.204.1-2 [892 kB]\n",
            "Fetched 1,020 kB in 1s (908 kB/s)\n",
            "Selecting previously unselected package libvulkan1:amd64.\n",
            "(Reading database ... 120831 files and directories currently installed.)\n",
            "Preparing to unpack .../libvulkan1_1.3.204.1-2_amd64.deb ...\n",
            "Unpacking libvulkan1:amd64 (1.3.204.1-2) ...\n",
            "Selecting previously unselected package libvulkan-dev:amd64.\n",
            "Preparing to unpack .../libvulkan-dev_1.3.204.1-2_amd64.deb ...\n",
            "Unpacking libvulkan-dev:amd64 (1.3.204.1-2) ...\n",
            "Setting up libvulkan1:amd64 (1.3.204.1-2) ...\n",
            "Setting up libvulkan-dev:amd64 (1.3.204.1-2) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
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            "  Downloading stable_baselines3-2.1.0-py3-none-any.whl (178 kB)\n",
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            "\u001b[?25hCollecting mani_skill2\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hCollecting rtree (from mani_skill2)\n",
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            "  Building wheel for transforms3d (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Installing collected packages: gdown\n",
            "  Attempting uninstall: gdown\n",
            "    Found existing installation: gdown 4.6.6\n",
            "    Uninstalling gdown-4.6.6:\n",
            "      Successfully uninstalled gdown-4.6.6\n",
            "Successfully installed gdown-4.7.1\n"
          ]
        }
      ],
      "source": [
        "# below fixes some bugs introduced by some recent Colab changes\n",
        "!mkdir -p /usr/share/vulkan/icd.d\n",
        "!wget -q https://raw.githubusercontent.com/haosulab/ManiSkill2/main/docker/nvidia_icd.json\n",
        "!wget -q https://raw.githubusercontent.com/haosulab/ManiSkill2/main/docker/10_nvidia.json\n",
        "!mv nvidia_icd.json /usr/share/vulkan/icd.d\n",
        "!mv 10_nvidia.json /usr/share/glvnd/egl_vendor.d/10_nvidia.json\n",
        "# dependencies\n",
        "!apt-get install -y --no-install-recommends libvulkan-dev\n",
        "!pip install stable_baselines3 mani_skill2\n",
        "!pip install --upgrade --no-cache-dir gdown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "nWP-k5bbvC5C"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "    import google.colab\n",
        "    IN_COLAB = True\n",
        "except:\n",
        "    IN_COLAB = False\n",
        "\n",
        "if IN_COLAB:\n",
        "    import site\n",
        "    site.main() # run this so local pip installs are recognized"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q_jwRVKoPRXs"
      },
      "source": [
        "# Robotic Learning Tutorial Part 1: Reinforcement Learning\n",
        "\n",
        "This notebook will go over a Reinforcement Learning (RL) baseline solving the [ManiSkill](https://github.com/haosulab/ManiSkill2) environments. We will be using the [Stable Baselines 3 (SB3)](https://github.com/DLR-RM/stable-baselines3) package and the LiftCube enviornment as part of this tutorial with state and RGBD observations. Specifically,  we will use [PPO](https://openai.com/blog/openai-baselines-ppo/) algorithm to train agents.\n",
        "\n",
        "A single-file code version of both the state based and visual based RL tutorial can be found here: https://github.com/haosulab/ManiSkill2/tree/main/examples/tutorials/reinforcement-learning\n",
        "\n",
        "Section 1 covers State Based RL and Section 2 covers Visual RL. They are both self-contained so you can skip either section and run the other without any errors, but you must run all code before Section 1. Importantly, Section 2 covers the ManiSkill VecEnv, which brings a massive speedup to visual RL relative to other libraries by returning observations on the GPU (supporting PyTorch and Jax tensors on the GPU).\n",
        "\n",
        "First, we will import some packages shared by both sections and define some useful wrappers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "shSyf4FQPR22"
      },
      "outputs": [],
      "source": [
        "# Import required packages\n",
        "import gymnasium as gym\n",
        "import gymnasium.spaces as spaces\n",
        "from tqdm.notebook import tqdm\n",
        "import numpy as np\n",
        "import mani_skill2.envs\n",
        "import matplotlib.pyplot as plt\n",
        "import torch.nn as nn\n",
        "import torch as th"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJuDRHixXPw3"
      },
      "source": [
        "## 0 Useful Wrappers\n",
        "\n",
        "For state and visual observations, there are some useful wrappers we recommend using to improve RL training and monitoring. Namely, we recommend treating tasks as continuous tasks with infinite horizon. This means `done` is always `False` until a time limit is hit.\n",
        "\n",
        "Libraries like SB3 handle this by checking for a `info[TimeLimit.truncated]` value. The `ContinuousTaskWrapper` does this and also lets you specify your own time limit with `max_episode_steps`.\n",
        "\n",
        "To record success rates during evaluation we also provide a `SuccessInfoWrapper` here which allows SB3 to record success rates while training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "dP4cuYHWX0GL"
      },
      "outputs": [],
      "source": [
        "# the following wrappers are importable from mani_skill2.utils.wrappers.sb3\n",
        "# Defines a continuous, infinite horizon, task where done is always False\n",
        "# unless a timelimit is reached.\n",
        "class ContinuousTaskWrapper(gym.Wrapper):\n",
        "    def __init__(self, env) -> None:\n",
        "        super().__init__(env)\n",
        "\n",
        "    def reset(self, *args, **kwargs):\n",
        "        return super().reset(*args, **kwargs)\n",
        "\n",
        "    def step(self, action):\n",
        "        ob, rew, terminated, truncated, info = super().step(action)\n",
        "        return ob, rew, False, truncated, info\n",
        "\n",
        "# A simple wrapper that adds a is_success key which SB3 tracks\n",
        "class SuccessInfoWrapper(gym.Wrapper):\n",
        "    def step(self, action):\n",
        "        ob, rew, terminated, truncated, info = super().step(action)\n",
        "        info[\"is_success\"] = info[\"success\"]\n",
        "        return ob, rew, terminated, truncated, info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QmzX6177XYr"
      },
      "source": [
        "## 1 State Based RL\n",
        "\n",
        "In state based RL, the observations are all flat/dense vectors and easy to learn from for machine learning algorithms.\n",
        "\n",
        "State based solving is generally much faster as generating visual observations is slow, especially without a GPU. Moreover, distillation of information from images is difficult, and state may provide privileged information about the environment that makes solving easier. However state based policies are more limited in their generalizability across tasks and objects without additional techniques."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYkXMopo7bU-"
      },
      "source": [
        "### 1.1 Setting up Training and Evaluation Environments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SK3uvUwuoOJx"
      },
      "source": [
        "We first need to setup training and evaluation environments for SB3 to use in addition to adding the custom wrappers we defined in section 0.\n",
        "\n",
        "Note that ManiSkill2 uses GPU-optimized vectorized environments to massively speed up the simulation of environments with rendering (e.g., in visual RL). If rendering is not needed (e.g., in this case, state based RL), you do not need to use the GPU-optimized vectorized environment of ManiSkill2. For simplicity, in this state-based RL example, we just use SB3's vectorized environment to parallelize environments.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "bTCao9c29Qd0"
      },
      "outputs": [],
      "source": [
        "from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor\n",
        "from mani_skill2.utils.wrappers import RecordEpisode\n",
        "from stable_baselines3.common.utils import set_random_seed\n",
        "\n",
        "num_envs = 2 # you can increases this and decrease the n_steps parameter if you have more cores to speed up training\n",
        "env_id = \"LiftCube-v0\"\n",
        "obs_mode = \"state\"\n",
        "control_mode = \"pd_ee_delta_pose\"\n",
        "reward_mode = \"normalized_dense\" # this the default reward mode which is a dense reward scaled to [0, 1]\n",
        "\n",
        "# define an SB3 style make_env function for evaluation\n",
        "def make_env(env_id: str, max_episode_steps: int = None, record_dir: str = None):\n",
        "    def _init() -> gym.Env:\n",
        "        # NOTE: Import envs here so that they are registered with gym in subprocesses\n",
        "        import mani_skill2.envs\n",
        "        env = gym.make(env_id, obs_mode=obs_mode, reward_mode=reward_mode, control_mode=control_mode, max_episode_steps=max_episode_steps, render_mode=\"cameras\")\n",
        "        # For training, we regard the task as a continuous task with infinite horizon.\n",
        "        # you can use the ContinuousTaskWrapper here for that\n",
        "        if max_episode_steps is not None:\n",
        "            env = ContinuousTaskWrapper(env)\n",
        "        if record_dir is not None:\n",
        "            env = SuccessInfoWrapper(env)\n",
        "            env = RecordEpisode(\n",
        "                env, record_dir, info_on_video=True\n",
        "            )\n",
        "        return env\n",
        "    return _init\n",
        "\n",
        "# create one eval environment\n",
        "eval_env = SubprocVecEnv([make_env(env_id, record_dir=\"logs/videos\") for i in range(1)])\n",
        "eval_env = VecMonitor(eval_env) # attach this so SB3 can log reward metrics\n",
        "eval_env.seed(0)\n",
        "eval_env.reset()\n",
        "\n",
        "# create num_envs training environments\n",
        "# we also specify max_episode_steps=50 to speed up training\n",
        "env = SubprocVecEnv([make_env(env_id, max_episode_steps=50) for i in range(num_envs)])\n",
        "env = VecMonitor(env)\n",
        "env.seed(0)\n",
        "obs = env.reset()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imh9MIL0-xBg"
      },
      "source": [
        "To help monitor our training, with SB3, we can create an evaluation callback function as well as a checkpoint callback function. The `eval_env` will also save videos to `logs/videos`. Whenever the evaluation reward has improved, it will save a new best model as well. Finally, the checkpoint callback will periodically save the training progress over time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "fgAZKTBc-xBh"
      },
      "outputs": [],
      "source": [
        "from stable_baselines3.common.callbacks import EvalCallback, CheckpointCallback\n",
        "\n",
        "# SB3 uses callback functions to create evaluation and checkpoints\n",
        "\n",
        "# Evaluation: periodically evaluate the agent without noise and save results to the logs folder\n",
        "eval_callback = EvalCallback(eval_env, best_model_save_path=\"./logs/\",\n",
        "                         log_path=\"./logs/\", eval_freq=32000,\n",
        "                         deterministic=True, render=False)\n",
        "\n",
        "checkpoint_callback = CheckpointCallback(\n",
        "    save_freq=32000,\n",
        "    save_path=\"./logs/\",\n",
        "    name_prefix=\"rl_model\",\n",
        "    save_replay_buffer=True,\n",
        "    save_vecnormalize=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRxovjJc7gP-"
      },
      "source": [
        "### 1.2 RL Training with PPO\n",
        "\n",
        "Finally, we can begin training. We have to first define the policy and training configuration. The configs provided are already tuned  and will be able to train out a successful LiftCube policy. The configs are set to perform a rollout of 3200 steps split across each parallel environment, and update the policy with batch size 400 for 5 epochs. All logs, including tensorboard logs (run `tensorboard --logdir logs` to view them) and evaluation videos, are stored in the `logs/videos` folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCX8dfBn-r_2",
        "outputId": "413f91b8-2617-4cee-9768-dff22aaf30d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cuda device\n"
          ]
        }
      ],
      "source": [
        "from stable_baselines3 import PPO\n",
        "\n",
        "set_random_seed(0) # set SB3's global seed to 0\n",
        "rollout_steps = 3200\n",
        "\n",
        "# create our model\n",
        "policy_kwargs = dict(net_arch=[256, 256])\n",
        "model = PPO(\"MlpPolicy\", env, policy_kwargs=policy_kwargs, verbose=1,\n",
        "    n_steps=rollout_steps // num_envs, batch_size=400,\n",
        "    n_epochs=15,\n",
        "    tensorboard_log=\"./logs\",\n",
        "    gamma=0.85,\n",
        "    target_kl=0.05\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ocm_YIVy_CbV"
      },
      "source": [
        "Run the next cell to train the model for 300,000+ steps. The final model is then saved to `logs/latest_model`. This can take 5 minutes to 50 minutes (depending on your machine) to finish training. To speed up training you can use a computer with more CPU cores and/or a more powerful GPU.\n",
        "\n",
        "To keep track of training progress you can go to `logs/videos` and download the evaluation videos saved during training.\n",
        "\n",
        "We have also provided weights trained through this colab tutorial so if you wish to skip the training time you can skip the next cell and run the following one"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pZByxcv2_CbW"
      },
      "outputs": [],
      "source": [
        "# Train with PPO\n",
        "model.learn(300_000, callback=[checkpoint_callback, eval_callback])\n",
        "model.save(\"./logs/latest_model\")\n",
        "\n",
        "# optionally load back the model that was saved\n",
        "model = model.load(\"./logs/latest_model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "l8yYqhGY_CbX"
      },
      "outputs": [],
      "source": [
        "# Code for simply loading a pretrained policy\n",
        "import urllib.request\n",
        "urllib.request.urlretrieve(\n",
        "    \"https://huggingface.co/datasets/haosulab/ManiSkill2/resolve/main/pretrained_models/tutorials/LiftCube-v0_rl.state.pd_ee_delta_pose.zip\",\n",
        "    \"pretrained_model.zip\")\n",
        "model.policy = model.load(\"pretrained_model\").policy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kx0lALVT_Soi"
      },
      "source": [
        "### 1.3 Evaluation\n",
        "Once a model is trained, whether you ran the script above or downloaded the pretrained policy, you can run below to evaluate it and save some videos. You can set `render=True` if you have a GUI to create to live render the evaluation.\n",
        "\n",
        "If you use default configurations / use the pretrained model, you should get 80%+ success rate on LiftCube. If you train longer you can get near 100%.\n",
        "\n",
        "**Note that the green sphere in the videos represents the target height to lift the cube to (not a goal position).**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "9nPNZYKG_Soj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "856f71d4-b7f1-44c5-de37-6eee38f97290"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Success Rate: 1.0\n",
            "Episode Lengths: [36, 24, 25, 29, 27, 40, 28, 26, 22, 25]\n"
          ]
        }
      ],
      "source": [
        "from stable_baselines3.common.evaluation import evaluate_policy\n",
        "eval_env.close() # close the old eval env\n",
        "# make a new one that saves to a different directory\n",
        "eval_env = SubprocVecEnv([make_env(env_id, record_dir=\"logs/eval_videos\") for i in range(1)])\n",
        "eval_env = VecMonitor(eval_env) # attach this so SB3 can log reward metrics\n",
        "eval_env.seed(1)\n",
        "eval_env.reset()\n",
        "\n",
        "returns, ep_lens = evaluate_policy(model, eval_env, deterministic=True, render=False, return_episode_rewards=True, n_eval_episodes=10)\n",
        "success = np.array(ep_lens) < 200 # episode length < 200 means we solved the task before time ran out\n",
        "success_rate = success.mean()\n",
        "print(f\"Success Rate: {success_rate}\")\n",
        "print(f\"Episode Lengths: {ep_lens}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "-LkndEPa_Sok",
        "outputId": "f0a203ef-0f56-4221-846c-23d28c8c8b18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Video object>"
            ],
            "text/html": [
              "<video controls  >\n",
              " <source 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iP5KVS+kaMvmp4PWMIGnfECSpl4jF4WAzA3tVouaawouakZhYDrJSpQ3lHIAY/Djd9y1iMOg8lV5tzGfo0MJ7X7JnAaq2yZwf8wdOJZjh+6JpVzMdnIwggmrbyoq67Wv6HRxiGI7XlbEyV4qfNDwFk7kHqOi1k2E80az02m4EO8Gx61KPxTWeo8Gnlxs+FsFPSQK1FtioFNuatApVL5MESeszq55T6qVsOQR6nhtaNAKe+s+EVr/beVbeCFnWXMXFGDfCDh8xrOCKj3/j8q8j4Yj9z4vK5/m8+Ytfy3hmhk4aLEYY+8tGPFHwzfDO5Sjsf3ZNuOe72TOXfYLEKf1w95kP5LjL3IfmjXJPMLBfziDMKwvTflllkZyl30REjtJGitym3uwdGMyf43Ei/I2mHQR66gg5perPveQA4egDbuXu9tJr7R2FGZJTCroOzFSA7hYNuf/HJOuCKOoqX6RQTE2SIPKNzRJbaV7GSZeHxH9QsuAuf3O5hDcgMKAsdlrkiA9QuafekLVVPGXFQsf37WJtzbX1bCDAkj59j7ZA4ynH8NFtv0Ww5yRdGu04HzRorUu/lWEEyJKOCqb1dLvaBtIgd7bUqQ7tzhK+nF0tH43uBPjkD5YYXSZPG8nhL4CvoQvFPWV+XLNuRFHe7ajltODoqQdtLota63VR7h/H12P82Eb3590f9BR1cA0aO/C0vzX1cwUCJwPKiAurmLpNfe7MEdYH++1ckcSYeAsTgcJM+Da8qH0t8Zsa5xOgLoDG4QmKi2BDLil9yxTIBKLIuElk4RZg4IdZ5IjxDyQe8YaSCuXY4NjTJ+OxB+s4JTJctm9/KxzAclObYQm36/C2EUUC9MsTDql9TaUzCH5UfG0ejFTx5yMSLAGyR/F9bBYnbVlLR0OxLFuMSOBzLUM14uY49QHiNrVaueaFeiTFmoBhz29dK++wCbh3tHvCkqBo6CgOEqqfiXYOVcQBcC7jv7qsCQ+tKObH6oHPuF0Y9qCHys10MUv0dVR0Xj8plbhfLOl8OpkVEHDF/by/HIkHiuwmsU3+xtIVkZKu41b2Vdlvk7xdpdkXrdfAfdkMOHrJQG+OL09H+Jjdd6ZYT/EPFxkvOZ0jbxTFhWT6/g7IDGbd0gWcYlKVgRVHEnuzxeeIwFjlJQCASSq4gi+HCyNl5rRFdfKZQCOV9dt9SaL43Tgwe5o9gIHXtx3ErQK74UxCobBiCoFW++Xcg34OO4u0rkA4cH17hjOZt0q640DoMa0sLg5fOeWUBz3PzqAYpBVIwjMyHyPezD39nFxx2BDgAADrvwWu9t/1PuhRuWeYp3YJx/2QyGkE4ldTgRYOiuoXbJPPyTBGJ6Q1gGCZhY+gVBaPjP/33SXZoDiJ9Ad9Ra9rVwG1ZTRV19fLZ57ymjMRrP6Lo3yRZ/RhDH2JJcsedjWML7ipa4Osw7GxkTNTQ+plAlFayCF67f6y7sO5KjAo40soV+py88oLkA2II7fO6tk0+xD0PZXjsPIUEEiDPq/O9LmXQDf3sjIQpgrihUrmG8CT7vQM95uirz9e1gdFWkJ3ukaEKO61XtAsYfDXhtcYcs81VIxUUovVgvoFRcHa3tJGJw6brpUQYCmq7oLT26TamFM9mp0PoVk7m/TK5y4/dCXLqPmkVVnikO1bN3G+hPqSMDRgACEHtRoHI6TNaE76/MVrfMNkk4cOcl3ATa2ulihXJ/FOPdwYz3wCzkD4cFxLr8sCH6dekb5wBXVn8n9YE4BuwdVtdLqJduYCVsETtl6O8mBDAP8lYNTlL7xFieTZsspn9BDH965lB9Obheb0d2mnTQ+C8pBjHiOQf69xZoTBEaZ6yJrhl8xuZ+83PKF3QlEV1TakEBq+ROPuogPkMpREFreUcNLGD1UX7ahglaPaGl3F/PKnoGlXtJ/qJSWFhXtQBjIFz9B3ObH5M3cMSrsryooLJtbLTqUyeet7Dfz4/80SG+atXJfksdtLmoSPPlMbr8MUsjaLMshZI7y4wbhdyLwE0Ba0ltlte616BrNUPWrLQB3ZpP/ciwbU5AW03hDOodK55R0ThB/7mnlb3+iJU7WZrtxSWilOz/CJL11Lwz4PBJ8cXGUABWBwZrw7tiLGRo6qZ4V/KvB+uYTSeDRNhqgXj3HK6ajhaK8aEQzcv1VZl4PmRv7GCQXP8bP/yNzQXlvbcXkuJTOVb3/Glk032aZEHR0z2xgix/kSXQ5+qD3BKP+/1TeX+MDT5pGHpx1PwtVpfd7BQ8w+3uvWvr69yLkujz4OExi+ANUhWy+LVSfZBqu/yR51r3AH14/23BUQrQ3kbu9TYoKw6qnegd4ztP5uieo3ZJFI5q3FtVgUk0zMK4A4Fe98PwaH0APEShh1hYAtAQcm7k6Olkfv7wsPzfLQ/1QOCe9uKooMOH7bK5gdfjk8TNzggk6LNrij0CtkSu1RkNqE28pGdcg5/hTZVJj14xdsNaIpZcXdSxbGfgBRAjOzOu+BgtJhdrdrsk6PPv8iipJi80DJ0bhTcbCAYQ3AXLtfJTVTAM39jJYtf28cdN05xmJsEL/nL1s9ihRwOzM2Ej4LfMssdkxbc/fnLzV8DI2jCMEvdvyJW3UmuLhnDj6y9fS1W0zBiNkf+Sifrvvd4YWTso0OeO8wlQfcUz/hBST9uRrYTm6Nb7dV46zTg7O8TOZl0o2rM/tmNOnIS6hyPDqIovRWGO8GxlamtWOglAlKUuy72bfxAV1NfCk5LuR964V3GzsgtGfU8c1NKiizEhTlkqTp40OlO6kwkvw2fMF1ZzPmAAYXn3C1H9w+elwT7xgQjbjydAoF7g/zlqJwGIfXQtJwnNGXV/8QTGl4tYcyOm3G884BOJOq5Ayy538tPcFnFeB2Bhz4Frfq8SOv4QlxcB3y3L4OKDVdUBBAGy/V1RLx13lZFR1pz4NaeiHG88QAAUre28k/+tdpn5vm4cg7E+7teuQo8Wq85mTaBGJzg7kxzrOYZqKACFwVA2OBq+BQGeLWzGG2QgS4D/MFEKZGb+Y3kskATUdN5sijaHRp2kkMFHc3Eqg6G6L7wuoohg+NOHML4zYU95haiiDSJKY89fRMUhE0qv953oGYkzfHw9wcPoPva3G2MqxR6XroIGgCy13fbDdOIeFyx6ge4XcSlEJHd+LLbVX1HWDiwzpJ9G0mvxMAed49FozGfqieXgPAaLvpu3kzQGg9cHUZoMeQFOBRfjERqyp1enLsZypee6zi0ezJ1zZu9HPhSgMf+M7jiohMWwrqOTZi81zTU/KiKjz///vSHAPCQE5KBKOE6vFhQtLcAQC9/GIO7TB0QVrs7SyB90p0WqXHcbBv8t4pbFqvJmcMsNNTqwKWYYbfyywcAmNqYIO5Nprww81HgjMNuDJMWaVaC6VSxtDFrm6NeBcPT/zkM2EfIU1cUv5TWxVrK09qEne+bJxuba3qUD8rZeyPZs4ZP4vVmQv2epctrLifnX214mf4NuVQ+gbcgXyfsL4j2JpuUbNPEhho6xLM9M5oG6CmTyZqUZr+8yHShnl/bMMTBrDV72eKIIcn4tMD2Kzp50fSr0izOAiXmO5P1Sb0VDGwCsSw/FMhV2NL160CJZk3/BukGoMad0U8RqkMeULrSYhraT/9wcO3FKRI0iw74wZnnT1qrakuqDruKmgXZgjEKPzFXVWzyqP+nn5TtVHO2JWq3GQXl+KQKMK+uF0LkQSMkENNNXUIkOk3wRoRSTLGz9BO3uITDByPqjkWUN2p2beYzZ5MbBoM4SrO/6Q7moYAhciE4c9UbS29NEl0hbWWkCfi0u6fn61QknPmQJUiUnKxBm+BYej+/wa8Q7TlxmkkaAJpC9EvcoRrEOKNVEW1ETmJRD4CD3onXbKeopg9NNatvvyjWvOT0WQDSPWzOJAEUvdpuB8btJK/cneHv/CA5JWHD2VL0pz2g8gvreA82J/zeIx8WJHbimnzrmKxcZdiIvdQuw5Uhw8o8uQ6oFawMqV1P1ss96bldVN1kZa8ZF24sw8jMyNX6o9IqKyMJ9Aq1eHRCI4slj7oUiWXxlL5ldIP7aB9Qdw4vqYsMWX2zVLvEBpf/la9KldyCc4o/RUVq5hMKIovoMeMdY6P+MmYYT7KBUNresLQkH299zhR4n0umUD+0biFztXrp+ZvfFD4S6F4apWH9XlnLDH+c5D4dHHqHA79MQeLh/ESRAIUTajP5bw6JKqkSuLmrSGggBO3lnfy4IA07CtirtvElaHU/Vp7P86a4l1MPJYqwhhR32UcvSeo8M8iwcfSmmVZwCZaHfg0l2wO21IRmphvGuhGuW0ADRtN+38T9Pn4vAof//j1qKpq5HMsli9e1FSAvq6tNp9OxyHrJ+vyXMXEFrEksvd9sqDA/YJDMzbfMCbE71dGGsrQ5Ghwl8p1sRwXT/m/smhRHuVflwYOdOszwtpiV+C9vzhbbB9CFgrMK0ZyLd94lQmPZzHWDWB/VrM7cAAbH9yBptlSazgWrC9gs+vLhtkeJZGCDtliwNpJuajetX/Nj0+tKn1uAxJbCjUFbxYR4LJSAY6vXH9SEfHV9HkLYRoUo+FQuKgFXOw4O3JD8aMtzUQBsqFN7yTrL1Ac7ZgzMXlhy7J5IbGOQS7rnWdXStNuQdqovZxIqhMmZEMB+KWJV7PC/QOHV8jr2cgPrgg41r0AAcZ9liHci9svS2arDWfWtTf2QFuegMSgRF9o2UoD6pbKClJTphXmt4I7ISwacgcwZ8L/KbkIDeWLrJ71aQNgsn//3ZW/ZTZgclvXSCTt0FtG97Svkg5mYmTt/ttCc0jvkpDrdce1jKbgPgapC8oldQx3rc/xd14tzciGEbOPRqmIT5kZjaNSVpx4W84fA74eyj03+CJ2shGRxwfkJVl1FEFVuvEWkIzYHC+DHBejYN/z4ZE5PezMwxnYAAaTPvOvCz7yiIODGOleZlM3Vt9Z9gBGuDaWKfERFoO4idtRVNdiqL5nzvyDMkwup2Y+BbMbRCRpn/ARMxx45BzxmXEMHX90UkwvIDMqe3C5c4ROmZYmwgB/1hyK3u/0juNB9kbqe0IGbk7JU6DM9M4Eq1zmMEAADY5QZokbEX/ZI7OGfKprDBOba6hYu19nV+znG6Kau7Ub40RchrLjuMs+5mxx/huUzJv8nsSOshnvMU5gW/1P0EkPCi+/8+qJ8DmqMPFr/lIMQbI2C8N5w9v9bJSjksoS4mMUeQW4n7gkscNfjEwOlos8950yEAxHrpu7wBDnlog/zgK87GDOxzuBVut1B1Igp9g+nkKChJ/P5+RWbOu1NDV+BvW7gWs8wS/9MkDulxkH1aL8kO1sATWdZVNYKNOZG/94lW2h7lIPL4iuLOo0Wb65xOAd1CLAzxdWjytvfcycl3LYJOQUpgez2A3e0wY1dw960Bf5mVgjFsgkdGNF0kh7c6AFzZqrx/oC4Lx4xjC5a/9z/YevQ/5B+bes8yKJn4Wz8VNJ7SCMvbyuxrWACmXt91BOuubBOhFKyxtmTgYMDikAAeRftQLcDR2EApRnY20IhFEnTjyL57oJv+g2eWLg/hQt1Ages6VCb8q0Qh9wdF1PGL+52CKYz6ncdKcuG5sIRA7Xe+5mYoCXTozCvdNvHKrxVRqazJ63xGDJVkWubWbvkbW3ema+j5ddoX6QMs2BfXhubTuWC82sM7vkiliQ0h3Ss6sFFoxZnqzMG5r8mhfUpuDR0RYVTCOfMw24ha6Xmpn81Iho/nuVQDOtnZjvXKDAVR4NA9tCqq6EEEUWPQkyBSqdgE5u4xpWFDN8lJsdMFySJrntwRuEdL9G+yKSI4x23PZ5+yvAXb3AxLsVh4GurAJeqCvGvRDBqk6V1lDpQ1q/S5BxAa0YE2BjiZXcvWrW/EPZZtSBK3HuEhZv/BMI1QopYQUmGwpkQB05QVj+fJvfhdJd4Vm9x3vXXy29bEFPJnK1fSr/z9gVi9Riq+5i1O/CXsHRScIfKKVbMuDklmV0Z6HvSdmPuWpTvBkv7g9Ov6hgCqpD1I4KxCnPKeeQsp2OXPsj/yiLmNjkQ4uZGIWv3UtGq27pKo3OkNbsVIG2F2f3xRHpwznuphXLj53kH28u0y2owOk2sEASvSYG1+9AwnWsz2nXeTk8jOSKZTUyd0G3NjUfDaDWx4lltN9nZQihRrzkKUZZ2BoErf1iMSwum8jUJrQgO7f0gOeaw0PYk1pMA35tMx8oSBVasK4IivtVz0SBeGYzJ2FUOvMk4ra7voqflvcOPQF0c3I10CYsZdlP1vchIhumS2znz9xE10qj1mfQGqw+g2UG/KfumsgQnXdyl7DVi1wClcj/4f6MJBlyVsM8RzBLp45bT2eyOe8L3KFoL5HHKf2IKxFy1fNuFg7wrOQ+i2NYhHabPyx8RxGBBZx5Wl9+X0wIPA2WJxmmt0xwOUCuaU39Nefdz8rx58peSQdIsPDUF39/Ti+iu61fuP/Af0Pv9CDQdRebE4Kt4gFaL40J8Ahn2aIQvHO5OIKk9cSr7b41vXpXc8VK45KhBiKJHTTfXWMRJD5q2hWWEcd3vQDQ1EhfpLg7XYYUUUIvMWO9MI6U8f7Vy6L7eg1cw0nrzi52KHU38QQTuYUqbPw9Us9Qkzulg3RlfHCBBLvgSODL1uf2Z9fHpkwFxn+xb6y1ozZVRyxnFRnWcuG30JzhfkayyJyD2kuDfocSAuhN+5uCJea9Qe+8Tn+2lYdsdaylfQGNhlXgCdx1WSOX7BiRITVAYepg1mIxHu9pwPG0nZZ3Zl8bB0x9LoU4m/N0XUF76LGFemZs06cUCuUisgPF89ufAqXl7550Jbve+RSl0Cr3jCFoT4P5CV1sVWrSLjAxHcfh7KsA4ByZpwgBVndwr/r8C6pp5I9RQv9wgAwVRpVogFtOHDyEzwKnkwrQ/CCaDhmFz19uHTI19wQibIvjJtFM3PfOa9/Wc5oNSf1GakfMrD3Tve++lYIcl7f/yQ9LiwSXv6wwX2z5CWrNfmB9EkvVccnZ4KyGGqmEvJmeTHpljLnN3pVK28uTBwIbjpgfcUHTiUuNYS6SXsffdPXIRVXJEKAa6gSqDmblm1NOgy5PfdKYLIwKS5ntEFeTGWxkoYinpaf36QsbV/0dcG6WjufuhQKiIEGAycbrg8eeZwMZeH6YIf8Kgl8A6wa+qAHDAaSLznitMTQIt9r6vl1/h3qj4K2jisZRYBiu/VsVdFVvZJtTPDmHwRZQWZ9NWe2MISo8g/Ml34HS1yvJp+F5dS9tWSvt+ebDYTX6OtacaFTdbbwpdPtj7A65H/37e8TI7EdCD4xlpw+IX1o37TGSdoaVaMxW4gGUoN/QzFgRCdhydynU3xv+/d01zQGyMpYhw5RXjeqlGWRV0H+PvQusZpo1ddmKc9kNW1Vdi56KsP/6uQBbDc6+waDT6ugvkhlH3rR3PRwD0wQG0BoR5pb+PCnqYxigp5H4jdv1VtWTDiBDC/Y9+nb9fpMq82LNDn7ZFVUViopIHc2/D5LANSzN+I77adrQekN6OclJeyV9ZsZXR4mi6JFHaZ6ksU8IfJwqCeC/h16A2RwnULMjK19vShwtJv8DsDzJuEvz1VCEBBuf6Q+S6Dxb1qaL+wjSA6x6Qbtp95bJ9RZlV9LAIkdNbQDhc7v5cupM1Tz48HUPkZLPUjPR+SpOREXs31RiJRzQHxABstI12aVxYNlsyjPGs6Yw3dsVeSVRVjTfZSeVHBephSkLdOzkYwBVZw//KmFhUbpN/rjfS/5CSPW021EUfH2I9KP8qUU0e0KhAyMSSi+HBYC05Qs7MqAjB6tGkbM2tGIF/S8/iIGqHn95jL4MDceYgtgrMMkpCYMMQZUyxkMH0HNJPkxelk5agkbN07ZWaFjONTv8rnwFuEVrMsqakBzq0/0tFwyBvSOu64xZ4Q8W2n32oqwuockD9qDsT2nmK+2EfjilU0DRulk3Xp+HuBRlEDeCUAN3louXkBp2Si99wD1gKpZhMdhNTVnBHNJh0xG4BJ0x6yF0rcwCyHNe0iwThowZhBXjyKJgbX9Gp3S8kts2sQAs3foEntavi8HMfCwyQ1DoYz0t8JaCXkeatNfZwp9H+bplwXX2/Y6D0cQlf7wiZcAsocK05Lead0egNoN9uXUWI1D3dWX3P7K32JLQCXTXlyki7xKqcdY86Dsc9adnJbi2lQKkQpi+2/CzKOZWaRxN1m9T7gXJCJ3lym9jSoE/LyHmeC3q5fOpbJf6OAfS2l3C9tG2IJtbkPFV8Nq5AKsXvPzOT2zB0+PJUKWk4ZhlEoQ5/Pz273BzoM+5GWddboJ3iOdfl62KkEh6rMQw1RRNFfJkHRZb/0J8uh+OM1PaiAmEUJKK0ma42Ri/zq/Kzg/VdsJYzi02tM9hvGqs9dEe6o9dM7/stg6xyg3TAIigvE9FkGUmHk5Qt0/DFklP7gTveB1U7fILRXITMSNnQdP2X8zwRZHc2H/o6gZBmKC9IYPASs4X9SkRh0nDFYgYFbh5M+kQYu2mFek0yfb80CrErtbILvi+npJ4sma6yHqhQpyjfEXs1BoF0OxsA8sIWTWJqkEYyYMCb1Qmao8f1qsOiPuvUxztbQIrqRZ4KhPQfqv0E/1Z8a3n/gCEcZTuJNPB3aoad+NEx969P7PR//O6voGP42WdUZxTP82a4rp9A9q1uGK7p4YaqzDmIDFkdws67JuACuu45edxqjFP5vcd0ZoOs9BjoK9guBgZelmiK6cJIzl7rwGNhAikQ1sINvoSccaMusWmQr77lGDXfn1eSocAbgukmv1cVHdIWWNDmoVgQWWiM2DNQyogyv/UVZBrI5r71QkVun5xv+eFzqwDaYrFgrVszEKgZfz4v+3wyEecDnZ100tmrcM/gckfgqZMOgXgv7v8npWjtTZ5j6qd2kNiEs8JPv2O+Ugb1Zn9TT+L4DW+nbLIPAgvf2z7RmuCeM1BXf9dzKd0KAEuKyVh3J+Wy32yU1LW1myRCE2A9kA1tMAI/06OYuOwbyh+try1JQyBjjspJX2YuiR5IJa7JNmkbcJNNJi+4j3SrhjHZd1tobvdYTi0aZoZ0mDB9ZiwbQCaQL7uQqUxfDMOoFkgQ50bIGGp6qddNmxqWHixFn06A5wCbsDrTXYkNYpt/7sh7idDEctaSjK3g4ukA9tSVU7tb0pq/wlL/VjP9qulEIEHYGIm3qgBp/Q63RPI6m/Fr7hyyJmQawtPM7W5PPq/Fe9UXHcsiTVpCpCy5T5KDqiXNNb2FkebJUKEaXsTUQTfkOi9rTFBiJA7qHrrelL6uteCtyC0awmf/VBDHLsW1zDlDj20ofu9U97nN9BxgxMwwpVTDvn7UMduIr/NPHGp9xM4Klk3Ijog1X6JBO9enNkHoSRR6MPZ0Qou337Dbaa948Eydl9jQj3uBHtNj96QLqURWrD12XOrBSum0iMIyDOjzVy8NCOKwgK7VFQndrrcCMe5d4IU+uOpSLMLqdqKfOMiFy/G8RqZtE/tV4UyaPVAyhKtRrdKCOGXKftLCY6V2rBKPN1tqXhJ0xAApRRvB0hhixLDSRuPChuo4DiUYh0YFcHkxtrWE8UPNH4cJaVnOeJ3LxOr9e4KrKhAvh+g09qob3VVEtFt6/a0Tzy552QJREeonsJVkSu/2/hcADH5dYRbFZLweL806b6tPf4XqZlr4aT/UOUGNUCy1VfefhnAwk7rfgA5QJqXrUw/NSEnInbwKhatqGnT0cNe5Z8DZre81RK8Px65YsBShGBseDbY+xF3ey2uf72M514XPyVM5u+C3R3dLBFivNIi7LrimyuRz7dYuziGGlSvSCwnBpIZPgbt047DCfBOHs3NKEp+iVCnIIA63GJtKiWRhOiP0m1oH9+cgMevs0QfFpStAuI8cMG0q515gA1VFPaC5mRG/hsLfCwWCSolSBio6glcuu9U3OlpvjqIOVu6v1aLxmRg+iuQkM/Yd0CdLBEcR5DT5GRJM6TvX2RKcctedCiHMjpiWjwByGAhsuYdRmXu7OUzLBQTqkFAeU5zXvR65YITWR8rJVzoVX3pLrWqzjyFU+7wmVLLNwHOX4sFQZj5QoKqkQnFO0L5P4mpB7tg4b401yRTraqL5qZXdgswLu1LrNVavhOaxr460ipJK6U6fz6JKR9MBlKsctXhmn6gAoeHvboifrZBESX1Pi7iEabxwzZdE/mD0tzJAJphWsOTpS6SGuU+ktjfSX30mil5rgtzvrj9AFFuC/0K3NCyifI4cHKLDcigMc//pPmgej8Pp6U5RCVSBMuLYOkpTN4uzvAmWg977eVeBKEbL2R0SyYP7GiKDJtm/msLK7wA1htDAb+ipq30GQfbJ5SHcuTB9SceXIbsuMj/g0K8I50vhicRSDU2f+L//6fuI+jq0CFDbqY9L9t0iPWCgC3gn4glgaqK/sc8VrNc31HgDDJOnZdITEGfqdVXHsZi3hkqSKCsnRHrummlyfDbHb6W2sqAts+Q58nYEsVb3tydlZTvfE/RsAcBmns/n2WS/ISc5OaIXc5lz08Xq2CjLTpFsHCqE9WZcK68516AzTASW0rlfMspLcsqpLhdJtl9HhNwNsf+WsO/C9a7HeMI2n04ZnYMLV7t96Lj8QZXy+H61d6ni9SMlbbpLsElaaBeSW9lj9fBSml5eNmM3JumFOoGnrsS3An8XW992jiigh1nWjDbh+UuwOmtYLoJV3fynXu6HLTd9uR9IDEKftIPT1UT+YkNb3Fk52yFa8br83msbXbgk+ssaET86u9yc9VPXJ5EDWWC83Nn4MAKqrkAXMXVm1/6iL67IDtUnBhFfwE6J+C2+OnYX0yZO+vj0djt1Clum0HOcmwt1dUkn0cqNH+98xVLswWc3uz7vjsGX4WLBcRes4Fkbp3ekCffrN/tbV9FSC4tUyApJcQzxmrdT9T/aDhAX9ds3nNli0VNsfHQ69wiZRvfOkbAkmem01kb1TrQc+aPB0zwuIsAO+tfAlHBHigkqWksfjFK/KoGvseH4AGpqn6uqhbtYgeDLn8EDqNh5Z2oMy1qfYXnPEpLTPCNQI0fGn3R2oF2P1jR3W/aRezzrWQlcbvyoEGLAJKQLjsQHOVt6vR/jNLY9K8dJRzJkJnCS/nk9MwB68il+XSP+Iz8YSN3319cLl9ZcpiesUIvpFclWGgZSMpfomSdt4iK+UQoNEceU8Es0ikJnvKFlNqyVaVHNBl3+4kp2n4kdFPMsW1SKZU+YPCtVDF2KoCuTQ4qXBEbzAB9DkUjS0QUOTRZ0OcTKU4sik/Db+jrQD06JSN8lptQe4QFIBkY+fIQqucSZ5ztZXfGEmzc9OJjucD+3jZQlhk/QJuJB21LL20yYhmRhaaeaPTUuLm5mpTwYNksepPUHkx3XybnvXWlhNurhZYn3/DObv/XnqcDDsdX49iOmijzEKpsAqllHWaPP5+kEucSjwaHc1rOOeem7mY5V8qb8UK89gjVjMUwOt9qW0kylJEXJ95qBVSjI69vhfBJd6Y/nNBVMHGEfQwKivHob/cJdEifb1oq084Ow9lKPD90Hgex7FU2XMhHVrNvm9+nTbxwjeRyoEEU9aqjLRZPQ2JR+Dqu4q+sC+3n5er2jbcV2i4tAdc4DLEohr1f5B5TH04ZGXpasmQTcmMpjBcCE5bWaHfglXW+PQIBQTXvxFKTaxGCj5WUundnATxPXQBwtu29AIUY3QZ28FRd+GBx3cdaeHi7kuXB7V60Qq+tQTViZkb0MDILhr0iSUC9YHcA8sicQUrqNqtqcpoNSP03DtBWekXGYoNCiCdpfzLEeULtl0vf/RV6hCGcSEOT88PNhG37J4nBCjsO4O3iJ3rGDJvCWCpbAQInaPkAhTfkqQfvbOzAOH7JvYck7pU4PIhnGqWtVqRIDJ0b99ERe/uvmw3XcTjmoFg3aw3tDP1U6ddoiQ8L4F3aCS6q99FxIqEP3z5V/WH1Axz57Qp2xUDsUxHCf342749OqVDO9ACIgnoF8i391wJkYGNJFKTaKAGDBLVN60ECOahlj/lYvOzmI289h+ejyZwKBBHz/iJTv6b3iZkGY+3BET+keNPyv+Ws6JqJ59t4NiG9Z8+4Nt1PacKLSFMP1ceyeu6eBjiC4UjQUgbZ+W7MBmhR2JR77baOyXnjVy+sW/bEg45yWsJATdbNTD1TF/uEHS3qwgI6KJhHgNzUuWnFOz/9dhUvfAh5v5ASqmXev5DkZv9CGbRiu2zCmEqjAebJfp3aFu24yPd6zDyfxfNpAqxlIervBlA70ILK9HkASz5PibS+jlvWjPfC2yMhDPihO7kMmoRE679h7Q9CwglPQD4h7Eu6INSCELGcFdtf4wWxvvSqMbDGVuZUfEnPLSRHPRDMKDG8n/i1Kg5I2k9m+3P/sZ40/Kym7g6Nvbexj2rJ/Vb5xf1C0CImBm7Kgcp0j3tCGpyO9pfcT+HPsxk6U5cU5hZkZN/BYFymtf+IKjybE3P8ZdKFW014U21fw9CtuhPj595k926MB6/NiIXpRSDxx4nStq4YXdy8/2jLKvIjAabytEcKm99o1+LzSw1HM+u6IfSs6+heW3hfvLMmEvFkZfQiHxKHcpqJOl2ny2fWWMAw+2zOzFTjc5+laUZjk2+1P0Q/O3/T7seGr63MgP/qcPZpUUANW+ifrf0qbLtp+1crndL0V2YJB92s0bBXTBZDONqmr1GjQl+P7SFOZ0OL84ajDpVg9a2okKWqfkyAB4tyk5+s6xTFjaP2aE+lykwOelnI9l16Ha7gcb0BMF6DYeqGG1TcvV+Z1zVI2tkzLDe/rT+9F8WJUIbI8prE6esmNKXFU3KusFXzW9D3rHWsSd2E2RIc5En6kVnJwaZZJn+NDOu5VNSaKu7+M2dRBIeSL4hr6S+nWBdlbZt0rgzQ++na7Jy70WaOEi8kng/Q7yWPBTVTGNSwou5GEe4YT2kk5OrtavwYAe0aKQuHqUQVA4uBX0ggSW6P+sMJBS8vXRvFQgQpCiG14bU3s/YZD14LpRtjK/jC+fqQaRzhg6ZatzNUmx1ukl26dtDsB/dCrQzCVBYPtGfhQTSLJ8jN0DoCnzI8dmhXp/+KyiwQ7ABtbgtB+jsNYEToUYYiAbKDpxEm4nGSZLmBoNL6+agML09Z2lFKPrNbVY7MDqcmO/0f4L1Y80alf6KR1r1xIsu1raoKFWYKEYnstyZbIhSR58R/Wg7xxQW3VH/w8/l6PAtoqk8JpiEwvUtHDQNFH7rU873ZPvGsAILwPwpfVqivaL2t58yoTt7A+gxXw0B4PTrknsvFK/oKucBTHFqVzg6ljjsm8CDWzqqfIfeP8H65g/aL3ME7kko/ow69r0xjfkVbLtN4JZmCnRcuabdSvn4y8ktE0FMCk+cq1f+RQjL2yBXDXltLHAVZDuY8wWPEOkDpcN8JqFMiLz4ShxQCPKTD0f9CTXOTqkY49QmXX44pSH6jM9mAjgkmde2qbE4jK1Ug9l2RNO0oe1rWLapuWMwUko/NX9tBeVfkS9JUyPaKPoLJ37rzcXOZfYFlqGooA6Cegs6v/kUX7W0Du36VsaLtFPQw4Pud4FVVpj/w8Qp2LbNBqjod/dIbEr8CG/8UxBBLD2ZQGUoXFeix28VDcaw9Hkyato/91zAjrZ1lij/IPKfh22JMWj5GdDOlM8JXDMrHiVeyPOeUvAcvaVmFPUJOFbRl8eVgf6s/tFik+hEpQslvUU47sFsDgFvrgmvPF06BF2ruQm2cQ+zGFUrneEsVtJ3MHleuwUFvL9FMCVC+EuDFjIVXzdWVNfQsUtuG3yBd0JTiHuBgfsUqDxpwy3snwIfZjYN8uPZPKb8PRcLcX+F9aLbOmQhe8eUc3ofWwjV3Iljgffz2LLcQx9AwbctelrLhcN97HJODqDMfxDBtU6ngUOCrqDOkOiiMftC4yu7GgtLH+TgqrskfOkUAThtK+GAzOkBXIyK4sbEiyXOMvJ6MyNeEBofP69NPSjKvk0iPnF/DnRfAGzIyiKl+0yxbRvwMMAwKwQF2Kw0aYWI2pNT243PO8GOa6ETz2Hht5tzqzj2PWHEQSO+8a9VeF+cQCT5Cd5h6MRFAvlhU/MH03wSgQgPaxipzkZdOliH5rME89cg9voyZUTcwcBf8i/yCpzl0QVeJcUJJVmHKg0JbrZR0kjXL74q5HlqRgPP7CjvNopT5gYJ7xn2YWOzCOKN4lgYDypUi4YGChkV/PBq5GVSkF6glQez5A8eassOfedtTVT5MoKd6M+RjBfRQFa4p0TpvWHrRrzws/kjuAYVpLXvrg0Z1wHMHVhXOzvclXWIOkwKOMy3XFOln2Vz5+NBZ2+ftyZWnCz92WdBC4rKaGs0xgJuNTOj7r4v0JqYxzpzh+c05kgvz5Rvi60tFavcnuLnQKzAkdC4qwr2FqJQ4y6LqGsAj6ttvi8/Sjf/1/5JWR2yTqzGYnVnu1cU5p+9F0CcagjEfBBTMkqTo4JrZ+YkuUMUtA7GF7Z4n2RfwcGrwRU89WMMP4AVENYmBS14YPqjkVbmep0Z3E9KDs5j/1NnNtxUPiGnsQRpH1ixCDoi7g5nDBDyPdtkpiJkpobe9rjmqsOeQ7JpOm/9NDGFtRqW4bMn52WmEJcz88QjXaCEyOYuCdaUAMlcWDKz151vVEtyeQ2VYr9XiBr7qxb+d4RJot9uXJyx4gvjtZbCWt7V4BWzaYu77hjTIAHkYA6NoKPjYKvusx5mHxtj226W3pJCBy0uB9TWQ6bWhEpZsmxXF30P/dau4dehz/jwJXJlpe9dgXbJqxUX3A9TETkp3Mqnd7MFabld57bA7qXYRQoR1mCezQWe9CDgnF22A1a/MM3i8a7vZTxyDbY6xWvI4pEEmCPlpDO5YSxD99G93jTMegUz9q7WDQqWEtIV1cyZ/XygD8w+sjgR2qHAnOg3HC5EbNfzxHc8L4gX/3TdvrJwz3G6FFU1fTd42R68ZD5sVzw7Etlt2gHzhlrEBJSRxcdFoi6RD9dgVICUT7UiwN4GaR/0gXS1JUljx165f6gKuMsIufLJIBYlEk1k5VV63p+t2uYCSo3c7zHla44ocPSN6Cv0X3bMXILhj11dQ6hUIXUwlyFQc5lhBz7QHlYL6VT29aDqdwwVm0OXFtwLyRIilKjM12UCwOoYgmjtavn2QfAhrAKJ9qNidkDv3PDEocn4ToHKjKwcez6mIuNmUaCJH+MOQYuFCb8cjNdTtyRHbT7KK0taJeuyj7SCSkCAuM+6ktYAb46KGetfEr8/l8UVLIgXeCiLGKNpvkvELBjrcjVpSw9FgcZd1m8/3FNpG7pd2Nv1+aTjJiv6m6dzWEr6tFU4Ar+s9YtSDFf5N+QRPP12G2x453nJin2tlcWI+zf0PNjgjRKT+WWtoE1e5qBI0h10wETTERLVQF9zQ6BPT35t7hER7XtGUbshOkt/XI3e+hfwEF+DWcgw4jZhHSXqh5V+uRpyKsd4Y04pXNZimsD7PiIHN6KXboBgWgGexLm7lEc6UedIajDM4ikboAOBdIrYJZOvFFdIUNKXOBU4IcvqjRAQ3wpsksoyim76bEy0uVgt30d7OaeBXvkRSn6/wrnH/+aRMj56r8ZvqYYGHKzbeLyCa76nPnGlEW8THzyGmnClR7nzGJ4bhiDOhqV2tg+NtQ4cQvXbo5pKUyyge3e3IGqFOeybxngDgntrnvk5NR0xURaRHqup93sgvX1iEBUkU0HpouaZCQEiBnT/N/2DfI6TI/z+OstfoqWCD1ZD/JkG/kaRaXBFm6P1l3v9W+V4WzIrcocn7iyPeRw+TS3HKAankum3uP/0/mLNByjrO9jje6vYMt4WU/c6rb/JmgMzlMFxXwOB07wsvWkv2eAXfv+AN0Wgvan2LSrdZOldMM500q/bSn7l0c0IX1HgbDDWS+41c/C5JbwjgiVdM0SxITnMLnehSK3XhBYISynYE22qFq+HiWHANxZqhd6/DjzM8379UVOn3N95LGurl07BMEYCfyvowbi/K/ejry19AWJXwhkklMz6hOgrKQsthbY8z46plbq5EIVsWEjc7t7ADJdnF1aP412Uxqgk675EJJChOTCEoMdKepEHGnEeSxkBQ361PVMMsh+TfK5hzi6vQE8f9cnxc00ffp+AOwc8EpoAx/LQFfiZhkx34pQLypD7OBqq9vP5vYmBZAdMIsw1zymxBJ6kJYwjv0IgOZkRQFik6kP351LZfEhXh2lkhRzMq91iYmnU6qulHUlxL+yOV8gFrQLU9qXSsNQAsjyvYwEyiS66VM+OCAV5VkkzDJDMCs2F0eP3nGKhhrHpFPO+7ukMn37XY4TUJ42OniuZKJ7Id38RDtrtssWhI7C2bk2KwoaWBl8lK2NYGLtU72LC3GCmW3i6a2BWQqnl86vi5eziiSe+yO18MG0EbBbjsj7n6rB79g9MzwUO15Qz86QFN01v0/rm5vyXQdFCOyaFkatqYTFuZ332Qy66wsxZRMkbICzUsHpcXC76FHlwAySj+tkHRa863xWlCyBIe5u70vXlPhuJP/e6ex05okxJ/f4i5jrWF7x0AWrkD02//+asCbNAcGiP5Veuai+kkvPkV4BrLaH6++pQNUdQBDFua3qKHYtjPidfg1EodnYA16JXE3va6geIufzVEYTxIULkC7OEcl00KdBXIrEh5f1smx7AIyhFzITNivEheL/4IGcN44fsSxjFEbCQvBwkrcPRF6DqR1JaJhtkNli8IApQVJqdSo37n/vP3yUmH6NyVMWO7O9jy2mrKn5JdkQEAFDr4IisXipL57zWlr7qVdZyS0V3ZcTJZeF23Y6lmOJw2BJmq2eQR6CCzTIRpxywuWH7E3CbuzaWzjZE/OTbNxUI+vrSGOMB/rWkpyA/aHkZdvyZZr4q1LtYCgldY8DuSyRWlYvnQ1GZrrJEGMXgOzuwnKsrBOnHUQ5GARHeerzO0nYGIy2qgH1QEOrq4ceTb1eC7g//0FRbW29gEju1hoL6vbUJfe64mzZbfcJJfkziuc6WC2B02nJkcBVUj49J7FJ41XYaWrUTBeujoEEcRmN9iK2CktbSP9VJr7s4Y+P99Rm2kKLeFHJvp1ZTzwHPPBKGy+pOKhl9sLG1gdg1Nf9IEdw4EZM+e5qadT9WViEVKTIAdlRVtSV4hXGhXeysK2HjOk/ZmvW83Cg887xl/vjjVIxdk25/06CzOaezWrJamtXU/lzmsPzwIN3KvGeYP+R6RCBIXjS/JdCaokwOlfDDTBueFx9UIcuYKJW8dQBky3qEIOYQixK1Ek+J8s8QvjAeZfs7IfBgm0W1YVjpeQDIgQgP2aHIQ95EtsZujtYjrT2GcE8OtaFusVXeHTq4biQbPhRu3QKaOPFDMPtH6EBAcY2ugPLnN+CtHcztJKWrkSEsXccWzF6bnOOvTiuObB1tUjzDguiOiGmUGWnt97ptdXNFkC0xkJ9LOzEsBN36dLnEnbU5KpfqTz4RfS4fPoyIT3AWR7eWgTiKBVKwfWwaZBfkb8n18ZbsWa3w6ZPmsNboNEjjlALeNJwgqU3UQVoIlhFrbe9Q9HYsegGsS0ZZM+tFlmtGjEDQEQz6YP0/lQdMzc2RZ8T04lFXZ+YAfFdQ8S9J+vLXigStwozql5EhGrspk6h/bjrR1kU3Za1bmuXeya5qhjaQwgBC/phwVDqhL2z9ID3IELn1uRdILj/bN85O2roDF/zJa6ejcNg+aZ7PBQy5mDiNzciQ93VQYXRawNSOvyZ/LXx9rHElW52O1byNu1fzsgOgIe63xvzBJs4OjJzJ5/IzFgLrNZ4TRng4+4eJg0tuKVezbm4U6lkoQrp+pYJZDZztEszzUoHrqAXpmQa/sy/tw+gLC53ncOCF1XpQGavNko9L2G1yxBrq3SrN9XTtP8NBxVt+MzorTY7BL6zWEXMW3pmFSYO179knOGAWXcn1t81Phid9odqqmdOVoBBxj/1VT3gmw4PB69y4RmFPYReIm95zyRFAasjryHpgwQpVmy7saRnofv9X9eb4Ua3HGVmOkzkaiWG3DgKchdkWCrVjMoin9PXNT0EcwWAtgxz9TEWy0AeFBMQKFwDNBSn7BvuxOGaj0WTAq4mqV950XnADiBXUNzEpMRHdJpQQ6HnZYFtB8h7NVyD1GFZx9E5ZdDLBxcLySDbR0xKLDRoPGIo4RA6AUTr4Vd3iRfmECejZ+7qQW/oEnfk1THXL1PF7AabMLNhwYUTD3nsVuX9eNPrPV7c5uNehwKt0NvSPhUWaGnpUOFG7EO6oLsFUEuAo90NQBDKxCGixYWOE8UZ70Fq2J7hoA0k/ANLrL1R0yNtWWDKCW6ZnYSVSZZz135Dcw/8KWtp4UXqcIirADMAWGCJfYV4WcjDr4tADvhbxhXhrJZIPPuG8M2i6Fo28WWGssmuK+ISlTVpaTeIIJUIK4kv7Xq8Q/2R5cPFKgqvAp6Mvdacwf9r7EpXYYZxXZomgerclkTH6Ha/BOVfI2XFNOXZp1/qplLPmMigdGPOboxnHm7D3mYoEf5nnDQNX8hr4rmDU0WdxsAfCmTaafxxddQ5eDLDikMa16Q1hAifsmM1IQmoi2cdctNEIMr2AFRGH4MzMMvUdkofUrihnFSPuKF3sKkG+ir3Gmav8xGOlJz2BiTNHhBv5grSkEWvIDjz3j56hNoICGGnllVUxQoExI3owyPPnDEqJWxkYMmzhiXOQ4vJmgsXx5NShgDJjXPWCg95JO1DuVyko6z5r9WGwX9EC7ndBXbWPDPPjC8fyaRiGUBqtKxOmrzqEQrLVkrnR5PsO/qpFI/AFQ4CzL0fQVtU5njNinMco2XU4eNzgVRhOyOWb5QfWbHz//78HKu7IDP53i4L8eTzjcSfhqxHhEVa98aizN5BCSH2zF52QrNif2yjqmE5l5RrdJ/p7j4th5UT1GARdZ/xHe4rJDXS1g1DlK2xZcF4N9nf4jUbbznPSKr+0diobOwV/RgnqGJi6NVZKNuhz+WpQ+8zcmfOTPBw/QVxbA2SbOnB1MoR/vDRiL+UzjEbb4GGTbFtBWRKhx/d1v1Ie+59LO1M5bAMHXva1roQgX1Eo/kSUd5rGs0xV9D8c9YAJRoPEa5m3I3jOUjmFFpiZPU0wL/04MwCUPkeqmz+KaOCbcFOlyo563/dju5699kITlDNEVaeLVCaMlkCjy33eMHLk1YaUh1pN5WYxE4GaCaGcVkbbgLHhGYAzhxygZyiK0gYJ2gKU6aDvSyWlP5IoSHrnASFqB0ruX20T3woQMDRfCzsyfzfp+HMM7Rr6e1tNT/vuCNQti+SwWQ6Lq+b+MeWa+HP+3B97zwY4S7phGWfZ87hEO/+VrczWUAhmVyKvOnfbdzfUF7GFkDPg/VcUyLa18/KFSP/9aiTAVNSA6Sh1d50x5Y4hbK89Qaiy321sgOLviZP4CSBe3DG17k2HYGzhpIOjljNRVju66GZdcvd/dlpXnyb9r+Nz/KD64jUz7R9+1wzQ1dyqoVcRy7kKB1OR5b0oZe2yOtlGq87yV9jDJids56p4WU5TPLDgAN19YbDtqWfLFrA3ELN4G3xWgQzNONqMb0tyb8zCWRbjNhaoALlDY8jtOJD7ZrI0RhLaRP/LhjqajySPUtBaZCj1EqGTXEdZ1DgAAOgAj6ZSu6ArgFdfdjZunTXm7QvjFXVqtzsBbxcwDKa6QwNV/xA0Feii5V+5Id5dt9D7uIg5XjVFqEKZ7+VVqn9ZCh5jcjLKwTv48zn9xUzYDP0s9eJa9Tm4AABRwsyIc/9PP4c8j4HTm/UGsxg8ddA5kBKL7jKQ1wTnz0ugohru9W5eLnfeeHCORUvHUyVMmz254SmDwe5jLuXAVEu+fconn2rEc4NzcgINZgAAJlGEkCdIsfOEp+zU4Lj5QGxkcT59d5oc6x/cdmEH1ldy6X6DsXt9Fy/KJCUjNK+yI5IJ5STSOJ/8Vd4HieldnD+fL6HEzEWRA2CGfhe9c4AK7urA7lMeH+ki7EXccM41a34QdguGs+umJ4naxWQASLe9yJcwo2WSfe25JWADJAW/X6Xl8nNChajVhemk3pK45rP3BNi9ZzFkVQpAOMfedO+9Kt6qo1YaaAdrlTDSrIJvz/rZU5R0RDUfYahMUineNXuTp+g8/8pPAe/WThjaoETjbvTcGMfpUVt9eZRlPCo6KqGeFULsW/QReTw6klL5kcVzzB04Ve8u8kEuSdsBPM9bKzz/S4YRhtXdSlOZYOhYu6bhYm8IZRD7E3pIYRc9z+o0kZ3XYhsFZRyotzqzd37pm+5gDdWORnoflFowa4VySTCdasKpD8fKK+gcBJm6amro62EaZ7LkP3/ucU/IKZqLKoP8S9q3PlWkaqGdoQ2Uf+2GEFZpYd5nT+c28W+pA6qSWL2eLTpGx4RtXmr7zncNBThZf+pdJ55NwiiwJKS0BvL7MbABgdOKSAr0Q3mvaCc1tbEg7EWxy+cLbq4/klbswrHJiPSTr/jc5LAVxCuILuCAe+CKgG+zCmAPaatzIiNsV9Hl2aJrJbV78nT37CXWXRU/zC9ZgvwhCW9sVOnAOqddFiifVVrQ0dc0asGNybZWu/Y07DAcBC8skJ5iWdTAz51457M/4HY6E3QKkNbTg2f75gFM+8ia6uQ1t1s2J82XlQMCTa4deqd79igweQWpdGf0pPkZLkHVcuHpoC0jaRG3xTcwRvZgErTHtrPcSpggYrmbCevc7H6DmT2avetN/CdSABGU7IVNHUPRc9Iyzn00/uTFDn5MxKvM6y7PrcVWNnjK5fdNduZ4wgJP+KgughsACdTiuq2LbAEdvzbR6wNYq4PQ/04wZ5csAhNV50SP/YCj2pbLXMDyplcLojQX4CLXEIbmBR52cricqMkcFEniV4JP4m2I1ExCe15ebL/dEVD1YbMD+O69NDTwODY8jt2tYolTmw4xFL1p6afG01k1cypBPxZO9IkP1uWleLtU7xVb+m+n/eldpyzkHfO8M8yI4k2KrVO3amRzoXoZt1kOz2AU/n60eQ0zm3ae7DajWnVVrxW8PKYevcB4JI9I4PMDZ071eJpeUZ6GpQs9LV54axst6Gv3E11ahZLQZ7efTMIYA1XlTYaG8+X9AuJUMbWVmUlJSsxUk0yMVPV36erQGe4xoVlWO5iWTRw8XQJbph3IQxksqonzFZg8lZlVdgzwlNxN8LDGj9AkYHa6wV4KceQqf/VjKkOlvHLtyqRWd4T7cymzWaA3neLB3UDN6+w8lRmUGpHHxrtEiARBovwbtQUZ//5YdBvT/8qJ9yIGUbAKScF3iyOq269aiFbuFCpNjjHdITaTrLI8gxPTuZrR3/ApPmA2rDdNOuPjOSdSifKh9tabL9/OOctrbFwdfRSOEbYSgJtD0mqEJe5Ay6IFlJKAuNK6AvLF+0qxCdwVjxP8m8xPnsIkUg9d/23ryHjC0SOfw8nXb1CMFYMXNOeQ2dwxyzZYNeNHzhf1GeDwrQz71fG3mhFonAe7QnGCOaBvu2bK/zj5vXYdY+i/6wcWEaabBpidW+qGobAw0pvNxxbzCVauy8TeNFUe77NJXrgEQjs5FBESzRJ+yx6DWhjo/JhbQUT/i+5v2MwI9Wxqob4IE0/0OZmyRAcHOoBNmFwkppJ+1tTO8Dkw5WozHfyxfFmKlbHroeGmf101Cze4OPJgL2/64Q01o/w9AFnQJ2QtWaz2uZf/ZgVmv7r+Y2aELw3+DPY8jz+XofAMdwc1H0caH1u5CW7gtwV9EZkNBBbum/GvVDie3Pu2ZRXSdig7CUZ+dOzVi5lS0Su/wcBPsimdrX8QCdFxR1zygFpe5kMpFC+6Q8Y5+2pWvUU7PJufbUwKOfTw2MW+1wlDNDx6wgzI24eXbotRGv+gJw8/EnB4MUFYt/DzcwF4PWD67WFMfPfcN3is/O7OtRLdt10+33/5VDuKHxfYinShzv0Kjo/oYMtE/KfThiB0cfa/qzFK0iyc4S3f+OFi0mGuQkXOMDCC5FTvg35UAJk/wQHGjWl7K+5344TJ2xln4pVncurboJZ10Wjw7NubwD53RLiX61Q05DhXfsyCND8SzMLpDP24GcJsMb/+nkdF7ur7mg+8gHERybR3bNczABPVOhU+uFZmmMC8bDzxJZqST8D3NhA+g2b5n1plP1K24ztMqbk0zt0NVxpSSA+ZbQHDCOm0xNTaAfqMiu77U63+oqtXalWDR/Q6g6fIwzwjQhcmOM0qzekU1fAwjMj2iugzS/WGYai7CnSKFjOWAaur/07dI8zk5/wmPO8HiAlP2l8RCkxffvBiN2RlJCNPgbIH/Zn/tAtT/WoIIL++jCAbTSmZNIEGcQSmYw+OPM4UXQr80QGkugfGPXRRbFzYXxUI/d2ukvNnpjpBaWkN/ajnjHZlD62p/hJpZVKq46WIqjZcN8e7OXUWmkTzw6MbAHgOtSmr5iF234FPyzIUXUbB4Q9H+zA7R0HBiAO2nyGvb0eAFVAG2s9ckOyxSYFTJXJIpO2SIBAoYWSI/J9bsHOCbFr5/E7w+bGBsqf8i96LzfnWuk7Wcwt3q0dT+rbIkyJAA18UumYjTATnvSI8axLfcA/FXtXA5jzh/pJtX+yCvHp0xWm39PY5dykSoulYT4A6iH28iPB1Pq3vyLnhAiLId5woexWjobVVDFNUfZYf+SIKh7pwcwGQBL0pVj4IbuCN+g9UYXRlXovaxz5lyIwCXYV/DGDjvXma/PWNHbby9JeS99/ZabMlUNe9fimhRlLLzVjLzCsvVLcZPf1ova3lVafyV9sfePvmjYW4c0Ya4kBq1DmnK5qRBWUARbW7mxjA1ZsimChLW+1zEDpjotOO8cY+onVkTz3+ihrtQTkOx49TqXvmDggdmn/UEhAsg2afdCgnQtcDx+x+QKzaIroN3M8ePIcm/cMhAbLwp4NzvwNtAJKQp2fGiuHCwn3tOOzsKD1EE5+8m/0ifG2Vp3HfORPHU3cHnfd4ctg9QqZRADUtQhsmRGqlTq+4DaXTMCJYCPhxXB7ZO9P//tCwjNdUey+yTFOCGfVbOvB/oL5xQSfYVXsHuMhAzCkU0p2OZuxJwEYrGw2duQ8iyHGDCrZA0Czr+N7CzEbJFdzUOun2mRVaBx3BmWdYVxdofs08MoGyhTlo7C1ITxqx3fROavRqmU3jCkktJaIkj9Ap+t5Rex2tjvAK16F4hA3o/sqHXLeuQHsXgo5zk9VTMqVvavhK0l/ReyQ/1G5lP1DGV8fR7I6i9tUPrUGbFIEK2iM0z6naWeM6Jg0A4lKZhC6KNgbRmYKKMKNtAOdhV+yJJ+PfaaE1v2p/mGaZ0FoLEeZjsKZxOC8oQJAsvEd7bfd0YRBW7tJ54HelDpDYHpnQZgsKzfizUb7SwQ0mGpunzHHeJAco9s3Ltw5Wc9ant3RzbjTCLZmjJ2fEkOn7HTae1YkfwPZcJ2fT7YcnXDpVA9+JPNvAae/yq+Pntg7CSmOtP11SoP5C8tabio9VfnLVr7VoJsGGzBikZ8TQ/Gvdv6qsxuAm2k9O8m5F7ODUBJqx8VVHpyiytF5oVV4XrtTzlm0mLU90/S/lR5GENvnNV8bIrfsNAAAQWkGeQniv7Jb6xwpGkf3BnouY9nd3RATEeSq0clCzmBuRltuRT+Kg8IHD0hNmuZ9Dz9AdKzMwRqNPuet6a5uFkJyGNhiS/4kZZh+jSsyCqQJNGgUReCHJU2ITdFtCjONO3gI8U5h5jj4swqvcdKq4RhkJrE+chzxmyaaptSrvUTAlwfP1+FS0XUasHD//zjNoCVVBG8u7GC5j979rwG/Hq4Vf3lB3M/thkdRee+YhZ/oHGOrQ7V0PuT1shSlFqjAEf7J120sSWSenWj9U2M9SMmJRZeLaIY6s+3glF+UGIQ0BSUksoRKuXxCEouEfktH1Ji3QXg5aZcbOaFE6xyW5dJY1+DKO3eBRkkInHYmu2n9rlYO4WYFku1BUUItmp6PB/qMCDXHDuoM3CQKMokcpbIq46XHgh1xyrndivUE1XH6z9ytNw4N06PzRmxv3GLxzv/1nTINq1URrzTSiZ7l3ooJz4NYx20z4q5mxTXzJ+EsGQiWThB7XmjmdhWHvf9bR3ZnWOxXtb9HmFVd3U9tvHGHUKOKdn/NTxB9Ir/Z/k/k5T7y73MPk04W74k1h86GmMLN1fpAQsJcyef3ToB91NI33DLjbgsKKyuevKnLIOXtNFGn0brJJCtkvla2jKpTJouF+mwSA/wUu8RQQs4fJY2dF5yL8oHuVI3/WEkc6IoR4/tNRvB4gTvU86zB4tlTnTwU4vChq1KIKG9+N+jVp54LcX+LDusanV8rH6E02OTFXOz2hjghJw5pJLKmoVxTmInHhXbFJKCL+sWUqyyefmRb+fHPxd0iLQPO4btB2eYLnT4uAmc6hDC1TV0m4ZHOiG5cv8fPjwZ0TtL7yT9uS3hXr84qhyVku6nRD8kmJtZ1T1w7xG/i7Y4fm8pSQXoMMV+j8jhGLPRPyvTF6SMd7CGQ3jFq4af0YSvmht/SA2D5jUQLy8KCISSGrCQGB5+dUO5P+n/TqDqSJ08NYL2XcE9X+WQEfdxMHy9nMHOVhYIBlLZM1An1ekhXUCIkjstfhV+ZRgDQw2ShXnY5PKMsDfLUZTCiwtg7dl/NhBV/EXBc6n42eKSWq+2tiR+PDugpgrcOnxyviZjVYzQeqrcgcuekvJ8MMxUFlistZEkBZ46czxrbv7z6ec/H5PF0bCH4qgCdH/qK4nP1JGsNNOSwCSVuydmy9yUigWO4pIAE6Z+NX2QHVSbevRWZX3ye3GZtjSaHMTlqcIxk1NEzYUTg/xsx5PJke5pJzsNryiPnLP8h7gcOlkgomMGVqD2l89DWB0ACrrw61llfXY3ogM+BWWSWae66RowTF/N/Flvx2xApmb0LsbkDhpfGLPsknQXr5tENZXfdiT+nJkrTSVsY9CsGW4zZqC2uSHkdeG9sRX0FPLizyhR61HaLjm+ILdjiRG+VvbcvAOdWnmcEA/1TM0xpppsEj3yCMNMQVNNErO04CyScmh6k+Ti773/BHA0HRF2GDTLUGCU0hbbO1YA35nj4mp2Fs5sH0bvT0v21loTxc0lqvWwPO5LRPDmmACRTUh6H3Mk71qid27YjSymQs/qjGI+gZYPaebdgSODegNb69zsv+zurImrFSoW3FKfBU2JJupwlFqLapYTOkRdy6p5AC3jbB13Z22PLNa1agiFHumdXSKIPCR0IKOCj0cfCACqoaWQ9CnXV2zraw8r2a8n+ttC5X/yWmlw29ebpcPyz0hI+VDzsFcfQgfioqKZWJLyjv3dX67FcfTKoc7K5NZO1TngL3+aEv8ROQztf77Z07TyfibJZh86218jvl5zz+I9zHn9LJ5OzFdBPn/nX/uVdM+7XSb8aHBS8eK9NZpltUX8OvABIPdKKaSjClxo2faFjCSoq4DKnBB3w1bIAhRHE3vvtLkoh6m17UA1AY5+sETcrrNOx9s80aPmguyziDePawI3v75Gr1J+AWLV3JUmZxyPfQ88uRwwBjZM8vSt7txUXhl8AnUkDuRUFB7CSVFobhR/8m4NSdmKkloPVrzRZ7Je+aPNf0fQBNyAf+zq1jOo8SJuXS4skqEwbxW47nbr2ZuqZaG3tW6MzH5G+QWLVGfZACwry9vg1SE3XVeHOKqhFI+v2wGS8LnBKTp1NbkLKt846fpP0ZcxUgCb2vzk6hDjIh2UHBrIi8xAmBxySYpnwtlBLkkb3q+4gwnk4rtb6A7LdYzEVdnkcR21oA87lYj8oy79DY6mIYruO1/W+TP2bSvwiPO9riodxxbaYuozuHiczhDAahEOUg1EQK/636a+xaoDJNVvV8kCo/nJTmX4bAFDmsbJjIpzzRmKktswurtonxdd84Y1gIpHJR0aQzVtIqdH6m56f2MqF+IGc438ijfaaAXnR+UIwLn3I8utnzTeA/aDbYfADtUoPhFbPZj9srxxq5e2zbjxfMO5TEyRpV2pusIbqm0Byc1jzvdWWErI973+5r59dMiqQbuzRIgalhc8AA6OAvU9apbBVUnptVZbDI3eKwcOhMeu5jdYwd1+/VyS58ul6wxwTR6ZqB4YPopYIUKcE6+7VGXxfGCPD62BvOLa5EmxDNeSUAsDdaYg+TH869EMqerwb5d0fHGMzeOcfsGlqWNTAYU23pAxRqWT427d8mN7iI9Zy2/aZxJHeHU+BkLiXVn7eRaxaGxk+mSl79i+D0lqnCjkwFQ8JSOyL+THmh5H0T9UJmprOr31Ejb6drXjmIUvia6hWLZKghj15NeNu/NzuFn9E8cWF7AHPDkI8vjVrlPl/LpEgwaq44OA97LFlxueJpM8FAIJ33lMCVFukH1+D6Oxr5qQGMR5P3+LfCvm0+DCZyvQkMUfqU0BdWjvAlTpTc1uXV1ofrLRO+J/4/4d5bLcwfKEgK3KgMpBREF3fCQWgj53VGXp+GXUT56azEEk13yEs9EVeQkTrOX50j36uRetiRwS7330CmCCKZcQrJ1WQDAPkYYpImLSGu6k3egKS2vUFt01JiWq72d1YEJDIPyj7YvvjenuABhXK1+trfaM97KC9vvtek88ANyiRSnrD8GEdLKNVX+Jg/jMOSpJm+hty2nX4b+Zk79Y0aOt8Ap6m0uXtHPHrGRZw/LtVU9I+NFyMPVSgXPCN2GirMk0FfYvyZF7gZJRgPUiCFbQL7E4sfG8WYL2LajSsSeKlU1kjcCOpVofhSqqRs4NEwwWRXEmkgKFeDVBGhx++CxBRDbpDqw+t4T+S9uVorjWvQ512PU3y4SkpIZ5qk8IMVJLcO2z1EjeV4sl4sP58B25FVN/HgB03pYIIhE1CrvAs82DyZREQiKdwxmWRpWUe+VJ5Qw+QaICtiy4WBoulVmJtreNSZvB8sHLVyEfHqwTPFpvs3a9phMAwrEcKehGRgVgepMKqnXE1YUJk/3UZxyh2G/J97CUs+HMVT1L6tfxftnoKlfyEEuNP4ZTG49LuS6OI74db7fEFREyKKG5qILEXJTQt3DhC0cLGMG74PdQ98vdc+W+L3x0QCnPtE2ne0c686spL+PArXkITrj01mOFVTUP65BaSLr2Mhpl8wv6v4CkBIlAu8d/gRPlFFQaJx6Xcs6xrQ0Kx3mvAPIT7Vw/iyazeA8RI2mP/xT0DKoekJOjpZABqYvMa9TOtPtEZvak/MJs02qvTedcq0aCtL3ozDO5IrsbeS2tJgkX0xPcYy4vwtzyYyaYPyb4B3gDHbhemWx9I9TqVoDhIRRYXyXCql6lc92qxFZWx8AT+dQ3DZ+H1RUCYCEDESG3lmuIdklVEWudCHU6v1FdFdbVlLE1Lp43NZirKP3jRVgFl9GFq3J/lcmfMQXaBZSH14JKDsyPTUcLsMQpFr/PmpoKg5mVQmAm5j1qU6WW4GPAcOybmT/5i45a8UcjlT8jFKAPOxIbeUDusStyLF8G+EIyrE80WE+HD2UJ3V36dnMXjbUb/Y1Pfk4WVRYxCAgm8DMKK7Caa2QZ2ReQI/L4UzeZkek0MkjGnXEH4+AO4dUTtuG1qBCP7w982eTLrzBXNNnRQXyExYy37lCPwX3H27cuW2ar8O5FGYjF1dCNxfBHNU6wGFF80XCzciYUCKcShlzEuWMpXVySxqy4QvNI/5cEx1ApCH8gvh2TFa30Bch3/fQB21RmtcRINV78fQPQkHgL5se9eJL+WVS+rpTxiZHDeT0hlL/SiHV7VujNTCvm0TJ/vhXHtTrrvw+Gj6ufrZgm9MPG1s7kEyNghLIsmmJfsTLgdU+0mvnsaWFim2fSUmYzzBQoU2hMhmqPXmR4FwRgZVNq9h/++xA1UAM5BBHbx1Xv2+vkEg40EsnhTTbaKHd+r19erkr7Vpl5wUPxzTfn5GRhg21v1oOTlzXzxylMtfq5TAcbOYyV4IGvFtyCUl/HvgTzDpXQ2cPb9hSEEnk/Kvm0l6frDnD2b9o36KTwK/BmDu6KmfRWx99I+su3tN0RpUjpI5qeKRJ4sdhfH/0/jYdojE+hgEAtSsTuZvz8pZ7kO2++Ypn+Y7dY993oRj5qS0hxyewfncmqsm5Ol0erolGLn55cBtJQBrNXeArBvWx2758amGdnIV3UBHeKwgJYFa2QIyUbT3j6AD7zabYt6E9QRxODwYn8RxJ3tQ0f5W5n40l2qAb7YwcqC888ezlmu0HuysfkRUm3mzOxWn4ztFyk0p3qFQ0VKkaYmcdX/FWDEoV/hmBGfOEynh1HqvB3W6hcEnuqTu7S+C2uyY6Md1ZQTQPHYPKqTE9yl2gzYxPLn+JiA/XvRGUD7M8lOnALrTDF13Csr4pkBo4Yar53l4T6JUlXwinFPHRPwexR9EcJbvMNik8+h/V/M/UuTpX2Jq3S5x312trvGfEMj1vEnM3dsmKHjTLHlAI5VtYjY9gYqtkFibAG9Jv8LR/2E0IMpp/TixQFSuksDeiOs0VLvSQ3ZobsQziPCuvHZd/eMWCWXfObSo13qQLe/D6vFzcEFO3Ib2pJVS1dJJMdMI4Aa7AX6dZ7EDMuGMByoFe1a28AueF+SOT46i4ewP/50r2xMJWEnX82istpyZXPnygZJDHD+aoE/ge9VHFoybttd8nKiwFzQvBuunZNcAmr5D+cbe0LZnKN/YFfjU1RLO4tMJ8OTDzKNKdtlOaIHcMZw1rwPh/FBx5zn/ZU8tT4f+EQ+MQfZCKyqEkWFsrlyInDrs9uQqx+IWgt25CaXy7CfjPEAAXTG7yuGokCuCIuQ5likPdA3D8SYdEnND/ZmVtUMe2XR99AaxQWuvuBgMPn/cl7ujKLqids8nov7mqLw6GItVbVZ02qKLG98pZtEQkRA3tDYPpNo8qyrDd7KZwpC+WPa8y/nnOR6rkMDunaZdTp/g6LXOUHFkLnWyO+f/7sUDU1f3ZOxoPQVUFsAxvsnLYkS5QNyl2YtTussv8LXeQ8nzMDRMZCN5AQEuFU0tnDgB0uoNY5C8iXtB8uYbzs9fPYRF49hv+GJkI8lwPrUtuLgR2K+VtPpeJUA9EUBHUg3ko6dCc18d/Cvq2ThOZtIpl+XTMe83ZB/6G8dR5uNuzHk0Fu/yU8/8iCcU7xP+ShlO4xLD4hwByWjCLqXpi2K9B9Hc+WV4gCHzTwLTxHlJH3EAAAtcAZ5hdE3/6ORLsqYdYVM3s9QVgM8wHDRadYjgfSe1pJ1DbCQshCJEp6FmAk82OPwBrz4WW7bo6T0a4IKwQfCxX1f4nv9Sjsi7krWs+Mg8Ui5w9lElWh1ONv69vR8pP8PTwApp9hUV3xAN7j7vcsYvsO76odJXBP0MXl76jNnqABkw3Z4ckDDf06GabguxZuNFSWtxPNRYu/dIZymkvetE560gFlc7hCgbZLEsw56rlGiIiG4kkHoDyvS9diaqa2ycfm3kyu+SFtgWBBNkdF3FqejOyGFatLVXZaaDG3pJ8lZR2h63kCKsaGpe0HqgndTP7NH2KqBHcmRubsfQRP5XSfDIjDwD00VkEog+uhEVdIhLUgk7IEiIKi4g9w6sJQELxeAvTKIZbj2ggmkvJ1M+/yF+NWDQqARV6sDC6NdbGZJ7Q9fmzjfSIJx5e/zim0sguLOWXoRcHfzq7oN3hCmU1pEwIxarPaXJZAW9Vq4dCvPbaEZ81iTjCj+Xstc9M0ek1s8srnzedfHFC8Yxop075jyzArHNQ0ow16NmnedG27K8Bj2aEN9VnnV1JGTVMoNLxx5bAXnxgv8i2Ogq/f+Tix5L49mPkgTyT9zBBX6S0uQ6D+gMSmuSR97fp4PInsRrXby3kyzHbM75FWY9k5nJHcmEt1NrEgZHZPtVWpj7l+2+AiXS8YyiCHM5Tq8d+a98N1QD4PqnU7XbVJ8SyLQfmHz0/7FhE2e1V6emh4u8bYvakXWVTIqzqMaiiUECiw6JsZLWA3eZ9KrWi7tdbxmWSiJNRn9eo1lphGO0lQaddi9wbXf1Qx5z5JQq3pGrKjjgJwWpIwqlSirRcIY4/BlZkrw/sRqH511zK929FttltrNOAMiECj3nM4oEnfGrh9lI55+Z+Np5CaFPG5zleWpCUl0vLIo6oDItGSI4bVpm2Fdsmi8yGvysT6vuKCCwqv/XU0LUHVEEX31uiqE4xU6ck3fBXbDUkNDgUuSqsyRHHOk+Uho1rDXEfnTdoFG6NkqH+AZbHIoTMa1O62SApS0EvoSQ/8Dnc65JxUU2O4XB8zdkjkNzXi0BRWHn8dd408GZxJOWVpAKuFgdjbJJDQNG8bDe0GWqGsSG4vqp7o+EWEfuz0yQCwmIpgg6itDsOXm6uu4FCDwLuXXutcelMNIZpGKD1RAE0ahopkaE5PoMbb9YjLjpJ1PunNaGRv/KDts4dfMg59Jx2QqlGGHdiqhwmRtgt/i6hMUn49CXft9X8LdRyAUzJFUT8xujGKeigEGE4PFdK8Hfz2Sfn3jQTM/vWEeRg0t3zdyiSKsyE1Vz9PxRcgo3oUkp6feGBon8Q8GXAlSUbQgO4k92emkpnMZj5bVNhNVcrlBFcw/eyMXYC+TwfaYkhYwD+H1zDrXxTJJUkBGqXaeTRRy7cK/DMMpP9HplTYXXNP2A9fYhDjyF4cMBCMaaDOVZioZbvRNMeKdBKIiscxhJ734xFdx75dAg5h5EOXucritEjQizLqkRKR0VT7VpHThbo2VY23m0Y3MF0EbDtr92FTRwms8XBZqimj8WEO8niDRS0TJo/xqduxCmgFgLG+XvmRgU5xKVrcWlcNax0AYXJPgz9xj5nael6WVQbzsDG+Q/wLQRaKrZH+QendiN0wH1/JfkALBFbsm9AxFS/IO16Afke5AqV3gRY9J6TvylEvHpzf1QJ0nSAL22RL5TFe6u1W5ImDR8oC0F/h8Aq6Kqw8AByGg5/GE0QFXl7VOIqymi6svg5q/U7vYRLRi2tkPY6mPAOG3BUZQIPNMaPh82mM4oae397IOMaUEBGSImu7YvXZw18tldulsN1UU9x/Aq0MS/k9zBKBnwQQ1rnPem3Q7K1KMUK3epAd9NZ9Vozu0jMzn7y2+SoPDa4XnYTNexBm6uQhKhOsiJHBVQ2dZlmcr9AqC/YnN2NPZuoYAVfZhCTGg24UmJr1BgnGC+afgK90TVH9kgLUWTMnpr8V0RvT3xc0DLJqvWfVy3K+WMwmgAkARM8hiBjqzFvSQJAPVXGVKM8TW1g3polBFEFYDoTZKuPiG8ZYaeZ7SMznE8C4ISQndumPazfVAW1m3jcAoW7D7/3yLyTBXAqnYV6FiSRFtSnvLN8pcSDwJ8XQiFrKss7sLGp88eaqOjExABx5pVXetAQOHy91IBN6xx91qP/siUIRXHmqzZwNTSQpzEcOmGbdsxJrhlAeWnIG8g2+o2jyQso9NvAmjEmOwo4Qc7xs6XYgo1EvHhOtEvoESW5EOPauK/WY3bWSO4N//UTHgl+dTXGjwzMnGVnFEuSitJW2TWZtJr0T9sdhgsMr9mF+DZpA4aTerizCab+o6jL82C6fXxkNd6Kn1JDAAbesLEAto1oj/m9Oy6o/2ygvvW7fdj2BOLzkgioEpLwScd7GgT7KbdHNWPQA/lfFGJCnuqRzdXrGFQfwUc4ahKsQCPlsc3K430Y1YE1R1l4OFKI9N4KJ3UzWS9hnpQyPMsHltvOvbnT1I0VXURaVJp/JYqHXb3ktpqACKRq3mnLEwWbLQrAS/TECfNeVDbrL75unv978zJidzmxan7V/uQi+nl9mJy8SBE1+YYjn8T8mAN8EoD9d8Hg7s3AoyjkTQANEgN+okDvwc9nzxdolI92aW9Ve2rOMuAZ1S2+8ahVIj5bpYfH3kax/yMi+S8DQYyJNXG28CAAXAoaGbpGnLk3DnsZaJ8ArQeVZI2JNuq5L/2524pxrF5v/WcS0A7OUhK5YhkTH1w2oJfw3vJ/w3ZhEaBg/Lg8npriO36s27PUy7IkdNcNxYPc2zKfWXKMzx1JfKQxhinNgOSrfX+MPcfWAgszTcmToImnLgAYbG6PnMNUV0010rBglxyqYCRoWjIWgAuv1GZamsgoBm0Rh9yMzo5Lc/gO+lv0VWzuh8Svj/fy/vJbPxe/h4DIIJXEkzRWR5RJvdpptLaigXwD0aK8Qk29+zeit4x0P3oIFIbzU5xTWEZP8W3ND97PsypEKTGt3ZXASFIa7MTSqmJYbVSGi1yxrUWywm9i6s+k938aDhzX2VoKTw6o0hVNatHPpnEOGM8AjIIg3aK2gxmsKJAzp1JplNMt3qNE6/On0J4OOTBSKsnLHhKxOiF6HG2tZ2KFlxKlPw/Jgf/rGSvXjxDKXWGa5qVAh3ywIdo55ETqLezKPZy3Xsri1Lq1ccwC77Nvmt6FX2WS800dgbYd+nl5skSmyQ0y7N6cQY5poM4uJzjc40JNUyj30g20d5R6rIAhfXPJgfPe2qaY7enn+WQcmQWJOP1zqhVrEB9f+B+mcZHwoQt4AMHhxR/iyoQ0+W1N5WV2euRTPktVIQniLeDvztxM/iT+WTPOiQYpQSX6vZsH6T6VqQMrFNHi0V8/1cJ2nIpmsKO/JWhlHPUNAg2rWXJEh2N8ONaMPgwd+/5fJ2P9xLQBvYEXeds9Ucf5gEm9BXQmQL/uOU3WoUJUOn0yC/VuMkArJwQwqGMO2wznd6sRsPYDBx8cjhAPUFFo0WCH8r07jukFlJcvvwoPiRNyoKw/jhZgAJpKT2QXAfJ9r+MTYoKGZGhuN94+ZU1B+p9o2cPtEhOmNtT7cfR22tM+8msYx7COhDHnw4IY58RvJFoDAQQASiCNsuuospLrW8AiZApGc1emKAEEBRC2kNiX336VpG+rkO9em3LmjX4FqnjEA3cd7EWji8Sc4bIrsraAL3rv9whwWIfVA9XA8hZWpzYtss5ammI2gfWMCyCs68vsMeC1/7C29h5ErC/MEzI2ve0Qbiteek/aYWJ/6r5UWm/QgTQtDEHkHl6qlavlNPmBpn2vNeYPeYCkrPXlBvSEyOE0X2ghaYTVboKOgsnuv7bG/OGT0tJJgAACYsBnmNqTf949LdyZ96amkSG6BRIIvHU+drtWlFz6D0uZ2tgnTfFcPo2PA2dcaK17rfxvzly84YUFh6GZf1aFycc7enpq/MmiV8+FIOxZc0hnqX7NmXcI/m+rl7njYBR6WasCGLwJp51Q6pHGXPsxphtIWngABvhrPH2UA609+ojrlKH9JF0shqHbprYu7tBbGZDnAOIK6loOb+zzt9vqgom/3bgqzWnmpitYPiX9EjI3ssEK6f9sb9bkNQAsLh6ZLD7qtoVqv1x9f+IfsYF0g1/QaQDSWvJ1WfMC2NhMou7OwBzXq2AYYIN0cFwIVq4eM7iicFb6f3LM5MxXJlR7EYCT993I+jX6PqF/Kc6KnN+1zU+v2degiQ5vZkjJw3JQ0yPgmWFKG0LT8MPeymGTKkJO5gWIjZW3jaQPfNjgWAKdL4NDQvH1I9NTNIj9/WNUc9Nda6TWwzQMlvn/aozNUUwYrwi63hUjedG/FalgdMcoQIQJ5JNKIAA2aOTzk+bp49/ZDuKTeMJj3djQCO3DyNVzdCHEjXWY7Ffo1ZU0PQzTc1ONd9KcIexjFiCkyXoVgaVjH06ekKwUcptV6QsCnRXb39lnBAa585or5DDS5C6fUmVtdNOkZNXqg1d/QOb69McZw6ahIv6TyQx3w1Ct+7bwjZgvGzR0iLCPxUgoMJCHFcBgpPh6SXpqh4b/pNk0zi0Hv3LEyGRt1WuMeVgxis1kevIxLE4eoAkuI9gQo/HPNI62kIEFQcZT21XPhZqGZl5nzPBUyP16+CyWlkD5+NepRWHcbpTQ5xNQNvpnHHFC9Ie1ZW11CSqBKDvkYrpM8KGHoV+PVAIDWfTn394XGRxyVMa5RKI4yMtufs1BGSh2IaZgJCvRsqT44twArYi1vp2yOZztQUtsL1ZIA1DOZVfVjbBfV7m2ETRAOaJphfdVDcAhBHSE21hS/HduBYcA1/dE+kz3dYixmWwtIirzNExgQlJhqflbhk7TaTQTnYO2rqPp5GTI66Lyr3LjnSJwuTKe5L9bORu+D/fc1FbtlUZkvl/ibqWpkxho/aoL9VwCuZpBB5dv75XR4ifkxWIyuSUWQuXpx7eyK1o46qbTJ4+cikHJLNuxIRlibVt/7UVysEGYmfDCpuOSisxXBZsXoea0MHp8RGFHx0cXMLk/i51qyVQXuyS1lRIbDcWYG8WxeLPJXOfXzIB55CcXNo5Ib1v1Ft+CwtmR6lh/72ixYFJvm61RJ8nyYtkHuevqjIKc/TJ6RZ2sPwNOX77uGxsYIPsNBiweSw22lmItyjnhXKOF7/CG2bLhQWZ9jMIS0tIdj1d6zIhVqjFF+qedkLF3AqhQg3x/mbbGbvcuXHPe+ySeDROy9nrx/sRaMsGeT8kLUokMBhXG+WXMNKf2aEh3x7I9YDgfU0TjZQ2k3ZWODRqrqi5ASApVjCfpqBRjd3+KMskEFT1tarOMP/xk5MhVa9mzs5JjiKJKEwN18EpkShxbI6B3kKO6+XWpXQ3vgoCORlsxxsu4/ZI3BW/ifBGSh0HvKgk9RcMOSsSI1NZz1KjWKErumLbFPg7syeQ267zNPmOIqxmtXV7ouS9PsCPtTrRLJFbNqCx7zZPbXEuUnGDERHYx9WBBmH2Kn9F1vc+StN3UMKxx/0gDYwF2UgNaGWN9OV3ggzZnrajwOTV0qkJKSt7Tgrex65CwvQ3kBd273N2CAQuLa4jKmHZW6BY6hW23LbxlbalzmPe11b9YaQzK9/zGjBBqWAC91PYg0nb6y/PbDn5l/KngwZzfRPDsz52gnO9U1x0DOZU0SLo+V1yVOywElZ53/h3oDW1Wwq7heFxa2qjnQ4thUa42ZSBKud6IZFUglhpKHRMqyYjj+PKkNQg2PfgpPleuWbynNw8Dk5U4YeF2hd+Y6+lJ3QQEpBB5ei+XKKuXoWdyeC8SH8CNp+XgrFiOvitfXEj8HivcUZiFAqw4zwgUi1ASB8ddyxOpd5EOVLuBusYyrHrI3KZqDP/TLqgrQuHFrUPdvXuP8kNmRr7wBRpab18oAty+JCHDsACf72EbFMk2qdQTvlNmSZoMuQBX8Dvmu41lO3Zo4BaPO0echMU7mClvocEB3/1ykrOPVTWEwhAEybO88cIMjn/MdBuI3jOyYDJqs8QMstptW5E6IoIrMF6ffVU5qj5suEU4M4/D3hNdQ+T5m9mxTMIHbkW+1t3XJJw0OlqiW7xavqtu/PEKOdlwsnZ1SqZ72iOSb4JhrhduavK+xKDMUYLDQzhxP3kyJ90x1lFLTpGgmMm2XisunGHqpLc4Anzy3lqo2lKI+Bq1J16OojeF4Bc5S2Y8d+3wcqRTwGp1TWilf5/cGUhdkMf35s9dt4EXqVdzPGHhIf7z9SGtaLrMUcqkREOOSqwUnpZWvSKqbXEzAaZscqmZBMTsWQs4n5GxtRAno55xiQLBf+xkpnhxh/Kx4yG4XJefBAt64zooRtmOO4EOblKQXCo+C28I13Uo8+IRCTYpoYWF34jxcC2qnyV+6/dANiK6MJgy4Qgim5AbvlopwNN7N/Xmzxkh3tmfwIN/npWyDbIN17ZtW5d80GzHE/IuZnUc0kc/7FYcEB1c/qTN/bBu9HQV6pqtI3jEIyw2JtbNe11c01hDSl1ko3laCr18aUKhUQN3PHV3YRRa+laWVLN4GaQvZdNd9E5tVuQRrNY0yz83yFDYZ8CyXrZmeS6wgkyOhYD6R5NXDMueK3AqQU75mESeyY/KGrHs5ZB9rbMEKRC309pUeaB3vUdvmSuLlzFvifyYyc1zOfETVAmxq3+RvUtDACEh7Pf6Af7G2Y+9mkOtnZJfqotbRb4kWaR3TMc5y+nDLb6eP0BXihnafNK1zoBPHKjZ7Mcx/Qh22WZJJPxAalYwbEC/CfyFR2Wyr3ZldC9ksVpibSDLHhpGDy77w7D+4H2dNgZryrZPjlL3f95NAFVwsOeYcqg9iiWiBNqmmOlO5VOoXU+/EHAIGo5S+r2dDmj7B1l3cXYIqHIwCrSKRg/1AlrXSrVyEkFO5ISdvMkFBkU6a77VaZbubq6cf1rzZOc/26GXYYJ/OXC7o1y1cGDESdt1CgqYWi209OZsKJgxUgUnAxGquwmke0koZ653H03SASVh30yctk12gAOk1oWgun54zacYsNUD5iwD7QEmNv37lp+6eU0ZAKLN8foEeOHY1oEuKnDz1q/k92KIUyZzrN1/5oikBvwUAFJXSvf18SLoNbmw1bF1KvtkYlvAAAToEGaaEmoQWiZTAi/5PoSM0VomYCSa4kD8vyCfxVn6nDwHcoKR1ak8A/9TeAyXrqFEi83zEI7TZ9YekzyB5rYTtMmN591sgzs0RSAkYOKwNEQFALM5idemhe4D4+n84srvaDsryOYlnaGBA5ICrR5sdIG5zDm2Dq2IcDMavzkto/X3RJ49I6L4JooJiWDmoZhGs+f5qLizv2htufvvrCcooxL0i/fd9e6gLkMXYiODl+Dy2Fh8RxrEzspGGqlxWUkn7K6/mJAErv0sCCNVFlDlqbIxIg8S4uSYk1yoWJKlTqkVIGgkMBDJSOBzzeNeyzWC37Yt/BBJKB5XZ7wOegGn4IIzDig8kwv1MmyxWSKpEIPH0nxoyHEXkL3pa2fLHcIoQGeamtVrQCd1PMFF2uyTM2fjQbaiLHMrZrUX83s9A4l/Sqbp8bUfb6hnsCqTcyAguHEGjjtrUmi83R1NXoBY4QsCqiCrV9mxxEtiwtqUrtJ52GTvP3Vmb1R0mbohWthS0wnc5RFWz3VqkOhvTCq52yM1lyGLNHtP6mzSVn05yDN8Zj4bXFLU5UEh28X1DeTl0tyQ1Bqew5yOidBow5RRZnpLqUeeKO3c+RwA401UQEEO397/caEscFNBtu7BmO+uUnMA8HX2Rs/rPuISUXfbzWcZAWFMgfR1DQE7xcPb2b5s3C6Rk5G3gtD9c815VTyluM8QQgzUJ2p/8CQ136iBiBAmx3D4M58Rli7ytmsEITdBp0aiMBW/PAW7xwfl2AXs/vRkuden6KrJL2sJDt532M1knu5TiVqWGgst3tZulVDqzqXXQO+aqe97bvd3AFE7DXCIz9oYRedvrqtjz+dhuBootWenQDW5IzwBpiMh6Qf+oniKDyGH7tSQH7leQNiBibTocdXpuRh+ENLfBg8F2HHfwNb+mXuryzrX9E+ycD4z5ZLsXjlf89rr1kSvkIyUIw5i9zv24R/m75Eg9BqQQksE5rxSK7VyqePlwQg/2Z559U/b7o7xIbrUi23CcjVhY7IebccAsdYkCyI3QIUR4n1Ip1PvgawbpsDPIJGHG50d6U2OTSLoDgIonGkChRafNEZy5WAdrUKmstEGuYYEDYTNwdFb7DKm3LKHyEVw36PoRNrbzgZFeEhU6rA7ZemgdRxtRTj2TU9uebJNAItrNXQOwKFeqprTuwympoJ9kaFPHogL3KqZUV8YklfkRb2/i0mFZl2JlIjccwbfBXuksZJG5C86WIpBwKjVnozyr+jsFb/DodtoCLTUDoCTjoL9KdJ/LNT62SIceY6sIhQF59kt0TzUgZhMMkS+st8xDU8pO75vrJnzHKWiCwly+ngg2ti05VLh81vx2PgGBucpT2XC6dR5LT0Sgk0pysJ+1qKHafO2pzzSCFWs648ODigB9LxiAsYQ188GX5QYCyP/AGMe5jl5H9AU1u9KGOPFoBgjTYubbUpO4jQ/Q3ir5ivslZJqJp//+JVjFd8/7CwxsCqCz6VDAx/dFZJsjAnbbX6lNvWpg3brs4HWZz8iUXju2nKc9HF5DkUaQsWpxNmkaW4AH5Kc0SDZKS9cZd83pQgaspbrgqchwkn8fZs9B3K/W0/jZKe7ugBLb3wDzGe+J0My/H2YfgTc7kmJLjTiNcTSufLg1ygJH5q9OGvqIjbtOKBl77R6H2ZYDYzCBDgT5n6dnMteSlzrl/3VgNm7Zj5mm1SAaMhek+Wol8jVd/iaQSNa6nRDM1WmJOuLFupMNTUZhUAcW2HxuQ0RJm9HBQcYCps3QhoAX1r/pYc65VNJdjWEZciifKuDjuhyPh6WWhB5d29ocXmcm14A67CucqMj7+bfPmIWWPwaf2RfLA9qzjb4I7IlaIhXaw+dSl5nyLwKSPLcwtgY/jTIUqS/XleZbxuFRmlcTzRJFkx5jvqqoAX76IB1/TILf0aLzUDTwYdXTGDsIil6GjOHzlIrvOa9abdAWFiLCDml3KIGyjuYojQuOg4htuRT4q9hebmdYN/KQAfjJ+t5t1pbaFn58umtmxRv315c6R2CkqtgdC2TbQmmWoBJKzsCtLQMDpR1o3tzkxxscoGd/ZB3m8MOSAncmrgdvN+/YXwtrZExylMIM/ZBQMI9fGLMcDgZ7VLsW3623JurhZ8PPRUWR3PlJ8bYDg6dwGRrrbaL8fmS5UHVoR4T6HvKyex0lnZyYpY0QK4sBv8voiLRkHClbRUEKbY+pKs7S0lCXHaxeMnKACPNCKmafjIYZZPKh//F20YGxxM8Rkjw3l1ZrCCNiNb/x4ne6Iax2Z036xKQmwa/lwNSg1Ql5QhXF6HE2hWUmhMZgXCQoqkhdRC0Ujm8MxrsSIE16PwW6HfzW3DDw3Y31BzgAYDeKWCbpmVUTI3DsibG+y+upcTupFTpjh7/8t8JBAAioNoWOUywmaKB0Iqq0rdPIZg8QtpuLZL/LH5lOGY55rscymJYHnlXPYiJkjR8WhexcAcyawfgyoXVykIEPHSvGlgA4lkNCApvMJZs+jWpm6PfkRm1t9j6+Wwpl+ca/m6Q3mkKG+8EMn6UvM/rAWfYnQnlxfRHR5QBVomHtswggj2NRgOaJQleGYc05+IdXG864S+sUiL4vp4zjZzIFZn57M37m6BO+fJoTq2SDVN95rCORPWskGQ5htiI3UKKvbKresJCguPJ1WQLh8vMS47PozqgT6k1qH202JqQwNaqfejqGuRbvItSPQ3i0zcApD2JK/mcRuutM4bHtnKlsk80mrbwhcZ1xgoHp9KifzFuQ/Zv4nT2iAi7bzp053dkCWwf5Z+vjBT8XbFHUylypMymtGlpzwWxWpeY4Zt7kCwpFkv7w9tY2LtVj0Wh+MUcsMcDB7SnjkFbTBPvC6LHurKz6dlegPzteJjWN8zgzP9OhtMocASSU1IqteFnF9QlODD/uwx3iH7SC3rsfx0iU1siKcu1SBXHjXf2bp+zNe42D1/4bfXTOK0fDZNmeWSoGOo1a2ZEv9c1qSnCgBB1dGh0MmpZZlCAs5mUUP/n1AzeEFMGEb9S1NxpgDRmoMu6n95pih+VP/wrOLE6pzN9jNM5tdqCU4fI+kdpJXwq7JQvoozlRR21JPb6Ippt/1wSexkMSHowh/wJn0tkVLrLc0bFYcGRh20Lrp/cyc/KbYwbl+bsvjYNDMRHsE/em5drJk2drvdYgh/8di/6LhZMuI/Bbe3VId84zs5B54IAKlWVjKFn0Zh5NFOfGiA5z6knoh7e4oc18Oahk8nBs/uULzKjxWE8LSpaTOJXQuoBPLJWry2T4HHLMyUkweDwT96hHIdONQtgFk19d/Tprx5hhp1OLjQNFpWqanXuxc6QViBuvOuys6I8clt16+ismWi1+m3x0WPzXAPldRgn+uySTWtv99tEVSCo33Z47yFls7HkLytQTaGv3gph4H4gpcf7kfeLzwGdlo4vhlL/fGzx9+bOU2NKJt+dUQPhTICuniChUOzfo0p2UW603dNsTY/qh+FV9jeW0bVfN05chqOa0IlUHozYgjp2OxIjNKuakFLGBdrfnGlbyPMAKcXcRZL/64PSarosNgBOgGN0/7JFyR3Vb+s9IikiKZk+vVIG8tS0KTqOSCF3fh3T4x8tjl3XLYKLt1CeOm1OxzSBZy92A5nrqyxoRC44KgEt7kR3uju0HJVkvQ6fI0TfLxjGZwTaiaVUkfEQ0cNAP3hwh/ewGhUDBeFdZnlsSJ6lXl/6Qtv0Gf47d2vEKrP9SVukDmROOxk3nZGeYTYAjjuJ1b8iz9Z5aEzrDpaT4bAmOzjjKjX+LTcQitCiJdW+zrrUu3GyNJ4R9DyGk8/zGbMpd51vAunultZ/45E3Ed775ONCLUbb+DGWuC1gQqrYXjP2Hxf6SaqiJXgpK7Sm9ezfk3xP65ey8sCagoCSO7VISv5b0koXqK2nPP76GCbncAYuHQq87/IkWtnE0b188s0qbn3Q6P51snhlSgFwtD5caiyRY2rB2gCv1H0QgSqiCk7JhbpBoo9ifghQSBFSegH5lCcLydN2bpelhmg89UURgQHwuES0f37Yy6XwjVKEQ34OcibIayLdLZwz/ipNQ+OXmuznpCMH0j5L49Sk3bzCRPAWToxKCYwxfSj5onLQulIg2Lj30KtZLe6i3mywrxmekmKPM5eWyras3obson70SBPIz0RwuvyZq7CLFXt3lDqikSdLT7No2BhMJFOmXPXe63nt3eWVmgtEoKdKwFCSJplqg/89qNL/ehTiqb9ilyvkUHhRQ5WEAV0Nd+PBGR2Azf2oHK3nxhCRRe4QxlFUtG5rkkqkH7bPWer1t7Mp7iiILqmQgSoV1rvyUNTT9D6mrZcql5BHb1Wl5xyCGfeEJRmAH6d71MgGIvX+X9QATx5mQLk4UkBwmccsH9omf+O4/4OBD5nVUphQmGlm70623ZHeGsr//orPtFZI0rnV9sJrulUFZc1Jtkr3N0fSNPFL9gkF/MBujM3m49fdZqwVAMPjkEkTcaC4QP8I9oEx0yrCpDRDnBnXlTQ3fAyEaJM+IBWO5oLAUBJZvJUyO1yKVQ+WyY4rggGS7K7dHuU/n6N27XiVeI1PQMZkk3jaNog/MAYN1wl4Q41X9FD67UPi2XjnvWwPeLXEf6r83+9sGOnrZ+8TY1ICvRZxygK/g8IoY/4eotc56iUIt5JDKqNMuuU3FkvGosI5IHJvpbU/98TA4JBRp/aKrjewaM04PMzEpGghsFNXCwXXVTlcaZ/A2XrOI27CjbWMSSTZut1EUCjOEKZrDeqH3XejxzYEBDZZADFzJhFbuG6B98E/VWLD53jSwWvsgjsmbpRoblR/v67DHxZEEFVqbWyBOsM/AsOeGNHC4aKsFK5AGEJ7Y4/YukR0lmrXu6ppvaumTxKdrB0FZiGMs63SFquO4N/0b7Rw3lpbJJFueqyP/gOtr90vgz6GvBPSFK0jdWQkqfrM0kBDYvvwHtCiKDWDnMyrshKEishUXjok4rpt4qVVJlXZMxpUTGkm9gnKhJUvzhhFlpzewmMoBgbnekuIZl97ZtCuMKZbXnw1/Lq4iU0qcOKmwEUQoLJvyh/gTjt5wM+Nj0At5rYndkQFT/OhMPhOMRDeVvsgZ2ynBcUW0ARItShK+VQ0SYhYx6TU2oI7GYzTYRGQUy8Kgh30KWTIqoYYep82VP9FrxO/9WtJ+0Cqdd9Q4ImL2QxSXj6XleZou/yROJgCsLUQ1RobZ35eH9QxYAx3DrnKddGjm8rYOIhRs2/t51oQrh9tmksh51S3OfcHWyD/LmKc13/ot18A6PR9PakD+P/w8hKKjnz0Nu9fAmGmODjXB0YqCbSz59ze1+cnn3zQ41uHmvOfUs2qFkIpus6OrPZOcgY1OUXQFeaWIb75leMdTPJS+9r6ImOl0tCgOYvcOs/HbigKaLTnWgxmmVUsE6fuSfchozKlaONWF2/Z3ECbUDPswHs+uo/crz7NOweYRXXI+0nh3Bpg/JUo0wDuNEH+lpT0GEA3uYdI0p/Kmp+zjBpU9r9wAKpJch40nl0ciIbYVFuWIgfzqQDUHvMCt31kkrF3k5eEaViaV40n5+f5P3llwgCxtU8VEwMEobqwfo/l3ddOjkBNdePnXgPE95vZHz6n02miBsP9D9cxWSGIrx98J1aeejOV3K9yxa0KRgWuWHST3A7TDGZR4x8xwKuYUzjuIZAy3ahCAD0R3zCoWqeyUG2AKAwQDawskKVuXdZHdNs4+LeBzTbHZRq66wHV68AsODxK8SrnsVZ2JwJvaqQ6HKMGyUWMSmRIlli4AoXft7xE5qt1XEAN6gnaYT5QNWP8ECelX/+sUh3K6vNtv7VXBXKuzwZYpLrUWnrkIEynbOdndfPNcsf8aU6qYXbRie0Xx3M67NdSY2tJhm1U98/ZYkehfMZ0qHHtaHckmkmjHb2bYZmTKbjVZf6OBp8msHZ9G6E/6bvvljztuDOc87ZsDsJfLiWxJ2Z8ClMp63vn6TpBHvFCutpdXTNkYXKxeTLejm1IyUGsm98BvV2Dc6HmPlIwzAE8W4+J5p+0ki9HJYrPu59ByEfonjqrQbPT/2JnwOWFFtU98K5zSFhaU1noXYbBmrtORRGe+zKpJH+N2hq++34gafpcVzCj7cbq+O2fEr/zSbCowLWZBnESy8U10vGKz98OxqBWvWKyf/vuqDa8nRHcuiGpq4OfmKAj9nvcW0zXe3EBtB5UzE6B6ZrljBRUyS2WVg5hQRs7qvYD0LfBk2r3aCCHr2LMClcBGoBJ1ysJn/57qmRvcVGdQFCN7q2zMG5ldF7b108rMUCulCJvvvzI1Qdlp8nyG3yoSAFnLEiY8g7TKBiotpnCFGiKkiiTHiqOeUsC+y1loEydqq+LuH/R599USh2Z/Ul4LiH804RlYRfzMARGj+cjv99kzafwDVQa+jvYCaKnvJpbE4PBKZJZxstmi2LCQowfE5hr08yFAs4EMRZa6YajgSuc+j2GoPNXiJeLcR4NHQWkTF1Cglqtaf1f+NBZCNa1HHiZsT0wk7QpgojE5GCZKhTC+6OEGPRie8KHvCW0pEZVUkh5om7sslI2uVSOfm7edqKO+e9zYGC5bWxBVAD4HsBEiIclJP8qhyhCelI9UVRbhTdoyHV7UlUXvRVbgW5X7L4HopLYEPJyTHVMLAiu2ASvozKeNaQkjl6AvoVFBKZnBUhAAAM0kGehkURLX9ujwk1eaan7c4a5uribTbsnNBDIXfBVPvn6DLyx0Nt+4U3sJ6/2UO9MepyA3Zki1KonWPoAkFtMK9SGx16HnbjlbWx+oXNqOk5vb+q9PglPEQqeHkA/OMYGN6LgdjCxwi0GKokHdk5fPtfCvF5hhpmBInvoeTyMtL9e3vIrYSj7KeW6z7nmuPTyCa0CYwaHdjXoJGZh5iC/sAm3n7aKKxJ9QFvX4fhopqJaK+jlynJK4j5ng23Q4kDPD2N+2DU5fZNugNUbNxqHfjlvroO2FV3bXu66+8lWGH5Nfg671/HtVbV4B8hC9Vl5mIeAgTG1Twibht0j5os29k/+8yrCi/UqnqT7uYNahIEj6GxEH+39DBX2CP0xBCHz5wbHkyegPRg9LV4mNnoZFBRGYQ/2YT/9oNMD6K3FICsffDswqItKEJxqVHKawVi0QQzpMQUyZ9iSyCioEnGS4JkkoegJMCn+LywOkDaAqnsJsBEkziL4wlYEfGIOkkbFgLjg8tripQeT/rrSOiLp6umrXE557yJOi7+/JutNbetoBoi6fsViyJrC+2CCG7IHXHJr/sBhkMFBvKUk4xSOwCeg51Pztsmc/rC4ZtLzTwmjMRX01XrVGp4zdjoCOs3h52F9I1+dKcOiOB4VjlYUABF3eV22b/4T9W5J7WiafL8DXRaED/j3iMjzZM9gOVeIVk7Mm90N2tAgwrkEusVEXyREVp0s9CZ/lMFn4T/Vizxg8mewbKY4dVt4csHIkJOGwoEG7UE9jR6kXsZLg2c9xU9MEjNq2zYaykyKUHACgsLi9JUCPZq2An76m8ip/gmPFWTRlUufRPhCx2jYMOqztUlh8VzyBwmd3s9trhgNfSmDngwWMWJPQN6cd1luiCYXuo5bWr0fWuYOxrhh3j8ujOq7/ZtnUAe9Go4uu0Gm9pQ1euIh7EU1xyWwyaZMoCXxDvtqA/eqgB2anlz23DqwhBEVDu7NagIpcB2KD/CK54UORsqE8/qLhKdgeH23ckDmaOL+q1FLb4lKxezCvj5CVd0fUGFVeCh5zgM5tTVzq9nLIyYMscq25k3bYEZZUyszNiarCVEwRs9CXijcreNQS4c63bOzukLmKYigWvCZ1a2M6rOYF3sfZGNmA0VVzt1b26ED7S0ZZfP52hQstaovmmqnFu/SFTThE3UtAJwCg5cBuT87hEh9i2BkaFommb4SJJ6/fknlOl29lAMG0qKHJG6N0I6dysnG/El39C3SRMZd8oMxkS8pADW8BmOmIIoJ48LgCaamTB5bhANiW6kH5eCrLZ11N7fpJ1RuKX8SZ3qXtpjOB/adVKbzwsluH+nsntsjuy5OKrNljXEHi9ceUh0O1Q13dD9L2b6BV2u5HodebTmA+5ZESNkMNOBSELihe+wZSzR2f3hJe6k+f2qx1noGD9i1N77PY+hwjcTR848GGurwD5jxkfh+WWjU/ZniZSsUs3IGYbIPPBBWFtUOop4eQO8JFasmyC0Cjr369/FIWKDfmWG1eWI/vZLbU7v9M59zTruBSOpgMTA7gEtOxKNgUXCA5ZWYBOgo2bsm/h2zhCT91d2GWI0fknuzXIdYKMxwYMJXPBUEgtMOmXVDF5uNXvJIqHjztaZir/+vJ0TpVj35K1jubacrXae9I2I5PdEML5xCD1kfL0s11wGsUt5OoaoGxw+VXE77sMZUYhd5LWSDfZepEGqaoBes5kWJh4AX/wUJJT2lxLlC0tWYOc85uC7g4u2nuWSPVvqNvaluYb6q4a0Sx9nw8McgZojLWSSFjrT0usjJAogbCeV7TCUeDe3VkhadptDNMdk211wmc+KMY4FcixvstsfwQQE4vCA4JmJAcf8bhGut/i+IHgSnU7loWr1Kh1jIC81517KWs4fhaGx+6UJ5hTSw5eU0EhxU4D4mtFf1wvtDS7frTHLUtEB0mY0KN6O5l0zCzilQdRtwW6JWTRU1tXfPHGUGsLPCLsaAQ7CXNdMVBNHTSkgdjfHDyI/A+eb7S8J3KX4i9n+kR3mvt611rXWkn1KBpyN2WIPJuOLxPmDqYO9ZIsXbGuMIQ/aOFShUR/y+vRTc7xk9L+psGnwUx3adhP+ecehgMZGHvvWzkMj6tfkUHzziJpPX5Jmwx8C8R81AhpQDKesRRryNNP7HpVi6F8hFmiPuwqOUCeTgnyZrL8/myfwdxLnD6xQFmP5b8KAg4Uad4+9hVfVBNiOfY3QEs2BEQBrYK9CCGHXREkEKp7L5yJKye+xZi0vYmMWzck9piXA4uiswhho+Q8ZkUAqh8n81LUZx7OsMJPyfUi02+qqPAL44fkV9myv9A9Pby+PqHU2RtN9KnPXBZa5oT9Q7y1tPf8o58amPZSRBNETQTFlLJwE2POb3RRaAZvWqr72wq5aF0NcLwFv2MkIVeM95NE1AWj/SA7tfcpCAKDqPp1pWqa/QWnP+2/lbiMqUaoDX++tDJlun99f/5tuWzWTcvYJ9TA5OPZ34pDXOoeaEkCz+T0by+wp1wSVAHoH9irTmyyx8jkKrakzdlEtBHslT9gYVdcbOEHT6gZX8v9urJt/zESnXPC3pWkNB1LZII6bOV4jss5s+E2lsWar7pJakb26mGfoHRsolvynubR7pxZ8vnACNLmhGGaTUzlF4DktWsyR1Qim/qJODpCej9aM73sUH50KX4X5jQR3+BLIGIg1N5MaKzi1sB8DjVRR3z2EnpmoDfiUI+yDbL4SmOCXNdI6kvGqqDmiOJS2ga1RTnr1EvIKil+2+Sq4P3kWOvsshIhhrXJ2TMxtCcENbzNcPM7drsJgJBZ5NGCUCxFh+yqrf+raDxA0k4IF9XV92hUsPQLHOgcp/VcxwrbZfnjiCHPCB4TXzoHZApTbgIWS733QFJGRur54eZn44ORsp4HAQp8gnlAXV6WQ/egVZJ7nVaotyp68TsKAP9PsU82f4+kDEy2WmIN0amyE6QeZAo1OWtCiLcQDBMrc9g1QV+PS0NYsq8xub/B0fIiCZzeeBVk2co1AXJy7xPyaYfykYJkbg6PTG+L4i5h4mdlzqP+Ks+NL86WekzcZAlU2aAQkkMOQ5/je4Dakkhh0YXTJksMc8V1KFQNFuFK19U0RmioVpu5d6J3JxFTUFb+hLI1cIeYOWxL2Zq6QxfZ8sfWuIQF8bgAzABF0imu5Ic3aS/QkefjVcNeGCL6cCtwl2FEytuc0aX6EhkDR6qqwBhT1KgQd15kFyY2jnEx1T9k/ILqOS9njk3Ah2dkIu+bzF/vPaJLktZpr7X1Ko3z1ZBSZ42mnW2w1QnUOsp6lI6Jgd1aKK2wHMMKRL2oy7Spjwvobzqdb4emidNJnpDvTZ3HL/AMg4XygmSCYx7vrQhtk0hM8WdhAGcQ1QeeXUeMpkUP9j+JL+zIbHSIUTgjx9sEMdAF2/IzFbbAJd6I1fHhGc1o0gfa+A6MPDkorNM3i2tagOMTQP2gJKVQY6ALQobLksI3CRGGfl2NkkkuirKL1P8N++AtS3KazjIOS7A+rLU9Tfk7J8ODEdAIbxJj/TxllqW8cK6FedixLPfBN0T1DArFxD0/j93ZfhnyWu9VHQP5a/bPmczSP0TeCMXcc8wWIY85aFS4sp7ZGSc/OV2OrqWWCXgeOgA1FZ1Mq/5cfvZhMI7xN8N13QWW0Tnjec1aIecdcnVNjM0IG055K7cWkOktlx9e14q2e2GSMG9CG1WvelEi3uH3DSPMMpLJ02Vkms3qhvlxLGFoM+ALDVbJNLI+h9dHJXUltqJTurAqL2H7dDpHLqdpUklHsorYsJq6V6XUnm6vD7YatBVI0m74f4LzpxClADqtixWWQbf5WhVz8yQoTMh8J3qG1xyDYrA6JL+ICCob8rMf+7jhlO+gabxhQr7ztMUODgSY13ZcDCZJAueKtmYDKm/C06+MShLY3OBxkYuym/jOfUfVNSanGeUzFzu78AorBGZN6aLH4cZKfNZI6z9PzOqtgjw0NuDs0DVa22U3o2/YWhefvl21pnPeqMTifi0c6ZXD+EOF61RkpRfMPMQdmw3AnLbXqNxsYF/zOPN9u0Kk8iCFVRFcgJEAIPMTwiuOxOKU0U6X+3ixyoZZ9uv8vuhoyWd015alLMuQT6bfGP25ETl1ZItLx8gH1Zj54S6AjaZnUq7h6OpLt50gcipOUfococsM9qR/7NWpjhBYtY1h9qhhjiyKOQtQAXVubICJ4M5UnWq1vjY6FGeSEaGpBVKPhtnQ573rO8SLp8jUBYhG1UwGJniQklxpl23XWCDIa84NxUOErwUaLw8OBFcJEfl0IXJ88FEyHlWqGLVJehpK3TOFb1EJ9/7gTznWOfBZUH+yaFqO1NSJohZ0eNs4YGwAACYkBnqV0Tf95ZlOrrb48fD5XBZY7D0NzqdylKS0hJi9TedZspvXeX9K2kiDWcRRPEnlh8SvkUVFO+pscV/49V4DAj/9Ia1ttIAMKkOVSLyFmyEnnKfAAMeEFDOheiQlsvo78gETLvS2f2SlmW8DjRmbtYAGMJdClZaVT1cA8UgsmvZ82AllqJzR1mpMSv5dhUCSKf++TqaF4IpkWbFNO314t0bNi96gUyapD3byDc1RhkuWV+Tco5YPLARs4DdzIa7tyBAVsaHKoOIlmKEK4bQCCFM5s8CqDgZG5p62QoWS0rE60T1+kwNs0jQORqJMyNh+FiSI7uXZ41+/HegmzZr0cB64a9+mQWSbDqX+5bpmlq9MvRXsT+o1GEqM2pvKFWAZnoDxKLDd1Q4U9dFOlvOFofrMMnbGSaOn+vPeVGUHNjcTou3n2RJlprnMkrKDjmjjfTur3oJUOUnbYeq9SM6CSlpfUrVMMJwP2+qdaZAPQumHgOGwFc/96MVaF/1EpXszEtfJ1WByhH5LO1IL0IiiLPgkGO+4TMnnVe0n4YMSNU1LTo3AHz1lL39CbKm73MZ3CaSOVEGx3Ihx4m96Zj3EBmEUh8gKdKnoV5g1HOfEEN/7PkI8/igbhlJ/sJ1dv2/KAVxx8SSK5bKSf1NYKuCDeCqaIg1TCzBCrJ+SvPpMvRTzOTsaNlOuSaW03JSqzKWlK1v2esdklZwKigUf+Povg4De7Jxnp0hP1IV9wqCoRS6UVQXKUR4KV0hSQ3mTh8dbm26N6Frl96FjMR0rUjTdtqPYqWQHigqqRVGdd/hoqC59x4v5Wyisb1KL8XfkFKbMhcGaEvtMIL1jv2BeYJsDxvmbo54kXJ5b3A1lBzRJ9PXkWa0ANdOPdxxa/AdjW0dH35EyfOXt/rr6fgS3ZDSePAAhCqZUdj+Bcitn6vliELGtVOD/jBabAWjfKYASswA++R2hvFBnLXJ1PrjlwJL+HV8OIZOmkwDpObe580Agb7UEiGJ1W5eY/mUE/5OGqjoLoh8NcvN6KAJ/zRHWQHYa5rVAC3I65fny7dS1PtV++8YwZw1E/X9iSu1JsyDXzA0LMpDT/IiFrekcnlhXqUd8p1fo0tfT+1eO2TtfWSHF7QYGe2UI6qV99H1DDut13KPDLvEr2OUXZsXIlfcF6BdLPqgC2ZF57B9jIPg4YMC36bG9MNss+DD36ruM14tgwP1aEJhYuXWdPZFej+MA7whaKM4g7KbkwIqJG/7sR5za/aXppw4fU2Jk6B+8b1cPOnzBaBsa1AEvVE3ZdqnKgh/IgzT0qSYi+2dpD+v/dCJqUYn8Q/LmRCzkiohnVy8Y+ndyRdYFzxv2aTo8soBlvG1/keDGtxEQBk7rhhjZ5sjp5Z+D4XrFSmhppqI3P+id3vyjzW77c/Mmjsl0YmJt0hKD8UqfGlZdsakUE8dEik8yzf5Y4n3SMN93Dl5KBEyQgwHiMI36sGMUQPDO7CXLcRUVlT6BzXQskDvyukHVvS7AKkTIOKHpu6P0d9yeZAhbZIp5IXHqYNECwVlaDKL9XXYDBgs9u0aAn6zxQLd4+dcFvalkunFpkCG5h4ZODmIef8FFzDEkSNwAm0/XhYpNwqX+DIq78M/KVTEAGuLxOO7N1zSqQ0FQZfeRajSVrRAX3Fma3Pz4QBsxNS8gz8CtSN6Rd+++K862A0LxOotqSMNZiCnnlolEteWUePlY/G4FTyFAcQa+0PGVDG8Z3RmHPN6g1rvPeK6dA/6TTFV2uzZRsgMFByLIPpZwszCgSrPeIRi2CeYOrjIJzVzCCnzoYv2iGbq47rASwYfoySPCJRd20x0nqquE8TJDgUH8VMkv1IWW2xEoQGeQlwJQPnfWiRxuBxGzYeLNav8qHpHIXIrRBl2VYlXg8vfcVW//Phv1OHudGleoPHTguwsdJ/aePvgqS7V+xNSi3xfIQ23m8bnKSPLfWiaYsT91dVikqYcfcnWof70e4xEfPVgpdsmFTOEmblR/UHfCAOsPVP1l/7vz+eoIXXqHGv3DnPoInlTCaZDqpRDWcwb8qqnr3Q11sZ3yBR+vaTz8UYwZfclTSt1VleXLZ7kzvvKxQ2RdRSrfdzW6OFl1dS/tT/AEdpFlPqyfouSYK/9hzObFAJEXKQV34NlDbqzelqqR3EYfWUkYxGofPNZrxLwrL2HcH0Dm64tJ4wu7GYv4fDW+fkjvYpQqbnwk1WAlfV/jm1DwvvUGE3FbDuSRVsI+Fy9r1n0ZSKr/QXj2M1OgRkzOKttMquAV59y7nmQ+DOve4Z91MmDqz84e+IHZGrNPOPO+aLvGY4w2KOh465gqoeLex04XdsYrLSNNCrImuv7mp9/r3qhZ6rH2ChIY1jglpgpJO1GbZbttWwsMmlP5RRzy6ZQUSPzlP+gNIJoU9j5W1g15G5WDebwKAQzVKj7tXvOJK9WJ7/T3sE9UOMoN33g1CYR7GpevqN8LBtQV5fy0Y50FY4WUJl7PMAM0vWhvtE7nrMlQhrX2fX2Yjc+ICMw5kkzqBfMlC35ZYWlS0QmTZl3TLTietO0Si1o0K3ZsPhQFPzWeudBCGyloUniMXcSbi6JbObYHmxTOiDaIY3u1J5r0QUi79BjrNIAiZJxjawf1MSLlrXrcVuoqtHPu7w5q3TV8pYSFFaWkbUa24qtIwnJpus8qNw7Dz48McvBZImwIuxF94WspySatBfm2AFjDxHtceC5sPHOJE3WhPQAvtmdUn0p2cutN6RHvtM2izs0BDJPignLKyXKipmqOdUDylrtpOqpHQCgAuds4EpdxqA3f4jDkz91qmq6bbLSqxEKtO9Kj3ukmFTBbVD/7UJrvZE8IPySUUm81ydD2vGnUqsz/XKiHj/BtCOTPnThxjYcsaZGTHaQVrUJwmYn2hsIWUC1sJpgZzJ0kf4Qa0e0tkn180By3dTADlpY9Z8q+a1VRUq9itfNhcMWTx7hgPxQ5zp1J0SwWMALs/l0HvjMEgzi2argPQZKkxGbCAHdQYJecid/vXkMe8dJSXv7PrIZV9WboEYzYrNl4PRhrLUjDylMnqIi2oLImnNRR7Epwq7+2QmKqnRNsIx06HUWcD/CbYxCAupRyaV1HtYRDrpkHRe0RCMd6X61AEWQbOnqQWZN9loVPP9TlbUXWNTA+8JPdEL2TZGLeDu8dbn/7xDXEBtDs7I/59mVdfpYtoba2EK1l/32O6O1jprPEbHd/Lt9KnkpcNMlYMoE2ZoUomrxkHgQAAB+MBnqdqTf91LxvTZfHxIcrMwshQO4qTEyuzQw4FPPliUV4mWuzuCsT6yP7yVdRBHBW/SCr3cAKYAdNvRewUQ1Xisspk2VC2ym/RP+1gnmjpjj1bbIJkFZTdEqrJfU11OOmtCxHWAv2+SmJfXbFn7BH3rEozvHHjdNn4k+gu8V3HFN90szjck+8Kn5oxSUT73XUBxsRynqLdXOMWin+6aX8O0sFhzawd3v/VfRFMuoEZ/dIxWMHi0N9UOtiSaWLanq6P1uLSKAwJv3n/six27oXLJ8VJQ/4av1RsOndVGoEqHZcELa4aNGik5pc1iT7TJqrH7VC8DlOpqMFkbGksM1QHkOhekHQKeixTJ2t/XdWUp1QnQM8V9G/fJn79LtRcSz6ETmhofSrmyVR4xEbVownyk1dIIYEo92O3EorvoatbMK+svYBPvh/ygd+8AruU26m1Aj8D7TQNDdGlcMIhrTzivXWvLZmxLQzHLXBxMjGKzLZe7zQ3JLHVMUKQ5AM3Y/qISBNM5RPCgOAkmXF3mOadVqy30g5aK9FXd9TSgt2DQIF3wvebiJ2pOGzn8jL4P5OTfDyVPqWGl9+8vGrFlh6PxwEFpC7y/F6Jnt92bFUooFhy+bM/boHL9cXqw2DzOrEd1+yMaoRT5laD8/5bBjwya//tbZimC8LEvytM370gkE75/bIpLXFXaeD4e1rC3em2byrSgrMJQp+H1E19INgImcsWDiFX6f41Aymkc8sU0Ka9QRf2uypsjRrecMNQ94EFwl2/jokVedMQu3wlid0Q54AGRk9oCncVqSdZIaQNq4B8B5zwdbQCfFfX/l6AbFjc4d/S6Smg1GvV2Wwf7OuhmaimlZpvm9azr3D2hdys2eb7saI/UaKbHYCZ3KcJKmLv4a657h2/q9Ql693iQracBaEAMxGjnsqhiyIkpFuoL8NgGQleerEY3L+Q7KX5krNdRpY/hVUdvu+g5ECNuhfim8RhdoFgCV9AwjuOdZcVrs8XLD5eZEM5PyKfuqeI9UuvkummdDu6Go58jUwJF7tPXRffI6OR/R58I7MfdZLrfs8y+xzxm+ZT6+kECOoGPAFS3F0WSxUQRQu2Idj9XvPQEsXBOWB8EPgSgtp7pTn5f1+LfnFM8pRiLD81lzjAvuOR0k1E5OUSxQNqF88q7mVTcR03v5PRLmQwFZPqQUj2T6FmZm0/xIV+DDZAApw3PUOwcA/uiAYAJEAjWrfM/+2QAirTHR3ShZOVLErgwZVTzQZS7k2oHLcuTt6dJW0lyytv/NYIsEkyNPBjbVPazKAHIGr+faKJ/d+pX1AXDDqTDfYt/m3AqZYdRLgW4aPE0WpQZfgoB6DBNbkpLhSJRG+yS37xnV11WzmBwEny5gDTpyPapGHnVCgr+oahEmRs3aySc2kVSm6jxLHPdcRAGS2FA1B15h5GONEnUx4kNQ+rRaxqjhaMy5gCr8dgFcvmH3vY0TEXkUzPO/E3JW3+kIJf98ti0pR1cFHkY63htOXPE3ww91wC/8RORmeFxfdz6sIeW/wWWe1W+mqUelXKXABY3nCnONJHGSOBaP7UqoiyBUFKxcXroBb7aRMmFq37WCUDVMRXdASQr+ycLcwxGBrOEMAIaubtdP+0U6Sf8NYWMVh6jVaBuZiZ+irROvYwNy4iTxAbFI/F+zmaK/o2gq3gE30088QR+y0BYjiG/1Hr7s7Wq5jAZ9kiQDIpiFZvkHhxi9e42RSSOTtdFr6ZVWl444d/wcTvGr32Nz+U8BiA5+X9MTVBA1nUfxYXGW3g5u6NAprsY4FXYUIGyyvUBG5DBwBsevfZPcTATFHdHf8wb9QjYESixeElGaQ4qMq9eot9vDuLXGjkHTlRIP/Dt6mzI+F77kkLtjMlBorJg4+ikNa6CsUZ/Npqs2LsZ1cuMxWnsFVv4rnp+KZzpVHqTt8RzXz6hrt8K3j25c8v1CjkBGvoB8uRKL2IM0/6EFlTV8z5SZAW12NyXXhyQ+CIfLEz6EDB2HZr0r42itPDcIL5oj2/M/EVAZ84Knz8b1s6OwwUJGedQR/Zd2rUSUQm39EcwEAtLLqFEdJiDu6DXvwcf3MCsDWObaPWLvr5ujrAToFgRt+TBU6JFIKVxkhXw4xdcJxBJ+FqOor4xHFegvRaw3LEwid88ogiDfiYJt5UhrCGxqfbRjT/E8NCzll3cXDT/7tpJOQVuFhkswgFwG7FKXgWmouJhSTA/bfMqRjweJw2pWdNLB7m1bbq8+wySvu/a4AYzBpT530MBz/FMVAIR4nDRJzUu4C/HVBZHdz9tRBG9XFxD9j+wSAi8r3h+nW/nQrT2iwEOY+JN6sL+YeS9KkX58Y/P4+s5DNQBmTEgha0lU3K8+EZ40VIL6TimMXAJx+W1LsF7b69bewNYH42fOeJ+xHqichdXgZikTawrMryPQUrDcBVrJcUKFBGKsg6tHWs/8W4F4QqzNRkh9MlKB0KkYId6aBd1MW/0iRB0c6KCi3qNYAXCoKIX5o0uSyYNfmVLk38kidQPrzzufL1H6k6t8g5Pd4xhx/qiQ7QJ15P5MAY2kIZf5GcvpuhpFLhj2Lfpt2CqdeQ4MOxMHfiYoklrveKwqyw13aqjw+U4hJtNcUCkffqzPW/bCv9WNlY0F2YjrIoG30kxU2iXHkMzuLYWbUebA6KhwktTHVoIa/AcpoAABWkQZqsSahBbJlMCJ+JFCTCaW9bGneP6tqCQ12Ud7//I4BEG+//tnwTsGl//CkpfIRpENGVel3bMOI3iEElWOsx8ZaN7jvufgZI/85MU3RKOELv4EVJW640U09EJSS/CkiCYJVPxfwqL5e1nOWbFG0ut7s+x2ufCWvzT68QenPoP3O58MACbWHo8nOo0i/tV3cbespF+jNQrDKGlDiNGqSc2u7XaP07g+1BmtBczq1o8GG+gL5h5lZ13gR+km7J/ol3EPMRlFofAXMJdsWTOfkE8lq0Ic2SWaDfczyqYNYai+RshHpimZ2UMdEKJrmXLwOhJdRtR5a5KgzWnyoc2wIc50tjtINetlXMFIthCSc3wM/gsUdaFNYZUDbO0FRrmsl2mENHmchU4cExb994JeOBu2W/jHaeyDZcxVWB1WZPw5o80ti0+zTh539tfwKfuMA2DnYivgxsC9a+tc90GB1XN0MJP9o29wNc8c2nXszuw6nQKjDcmmIKYRzvrEgn0SBLhUfopCTNCb44NJH7ynkk/BfvSuiZZURCDSgCVpUBP3qkI1VvivvU6P7hh0yA1LBy5wu5s8hY6mlgroY2DXJaxMu7Huas+ZQ+bhsfKCCLni+uZ7r/qw47VvL1QPDAyFfJhlH3FG0KlBs3fck9ftn3nzrDOfo4S1yfnfDdRSMutD8WxCu4PBeOBHP/npvR+PDdST82u1ckZoNgxpMK81AWOUe6y8ecZA0Q/TxRyOT36GQ7DKBdAw0otg8cBlk1BZwo8BMRgdWbk1aQwwanhkWp+lGY4sX+R6VHXtDQpogZAiimNhAmZCIi0EtpoglwFtZbXuZYxRycdWmu5TbLb+g47MWcMjmJ8hFBYU3CgWfaTGJgRhqXMON8R9Tcfnz9C3jdqtIx7i9WnFa0HvTsOl4yR1XL7m20B+jG6gHySU+5OE4FkBvq+LkNHlF4acbmrTjqdXZYzvSTdDC5JUAHdZv2aObKAg4vcZrBJYykBXkY6iiLhzpkwjOgyCL12NofOz4UimQe6U/OxDfC6gAzB9pYleFjS60KIzUwxVUNdrGZKtuV5zjWISy+FmFG9zLYguP9+moUNbMVrYUaD67JiE7HWJG4XPk8huFlXujq74bgePOJAsXSljgBGsKKAekontQL5XniBYZqGpxfi5MzvpvSi8ypnR7DfbZa7npQF/5bk8u18xXoOIkOQs2dbs3gUPaH6ytNCWWKhriP0ZY3j4NhzgfXyJH5bHb/VeHKSe912e6lBrGwLeTzloKMU2SyCJsNgv0Ki788uPhTSw+GkrxwR6cWlCtgr/4WG0y/RGbY5znB5yuIg039/E60u866xbKuHUIDonCNzyMfnjp0diadiMhx/nYStHNktLunp0zN5xVYKeUGFimbwHO3Q0S/ZZklhiKinClSe5KdeIaZIacMxTKriKSwTasI0jEFkzUS9c5ENBgOejXZnbSMlc38onHiW355NUNn/0tXRGhexookuhEKZ+B6qbRTsNKXwqNIb6Iw6WACm7HTX0PoW2mErSpr2/IXMKSKCeV42PllrQHzjTzOgzNgOy6mNe5z8mdtdtk7vLdi/0GdZJB4kwtUnfuwZFyVz3bNWJI1/SGnK/feKG/h89lkSXI9XSXqbEwmPhxafGyyJQkhNyno2djSeIJUyOy2ll/Ye6YZXeA//nJDB6ATw43gRhB3fkWqQ+49Ns9ODK4eL5QTMAYrMrXcle4qaGYjSMpFuJ/lwawxdLWzBYIjmWVU5D9VEX5rqbqsl2U5WK5CKBDJLh+3UW2pSTreH2B3TNTjdlYL9d0Khcon/nMDA0KDMElkto4Izg8yS0CxgNshZrK2de50Z8feYh/g/uJp3pGNAz+aJN+7slZmsCJKi8O+M9HUxQ/GNdqUgXCXLdYroOIpJZtnzQAVWkoRsT2vzhxMdNcoBgA0+FOZkO6sBKWWP8Gs5ZZkmwDSCwIdym6L1/KlR0wzzL5I2/sdfeyCPHkVeu3Mjq54aTlImIBr6AHZMa2hj85c+GGcIpbWaeskPxuUfX/i3zAMPN0ID2DqCsatCzkpMGg4Rm1nstRUT/85583BaeccMakRGcrXcypLzLGMTsaCozdK0FjpVspl9kOcZuLfwBoeiXIL/YKMnaMlge0vyKXn796Qhbtz9pACwBPoWp3tMOQsQ69j9T+L+OAVtK6p2MsViqrPHR01tr6oNthExFUxhaHjjfVmaf5ld+A+GzUbQRj9tjPGq7ef7PwLpFkco4lREIPemqk83WplXpyK3htkcFW8tOxJISwI/DjBW3oaYqqGJXrmADpJMTGYwhuy+ymFG1h/7/r4clJmzomtako6sbaA9eOTLAArxOV13dS9LDcNWN4SKQLh/eSkf+1u0j7o6z10GmgLblCG0F2IlIu+UAfpLHk6KzC9pFER0ZmqhlY8tpaa/LwvC1zqH1ISdzQoY46baaJpFMJAcsCJwuyGoa1J1IlKLRA95oitGxXN+DnmVqyovOo9FcxQBZVIqlk0eSD51sncTjFWbtagqLmzBJDp+D0nfEazXpZGuwAHO28V2U8/6gUchVMCXoAfs2DqnWRGkGIf4gBLv2ErhKQ/Df+wXO01YKRkX4Wgx2AaF6JNAcvNw+RkYH6x+W5TD7VyYVnNkup+w4e/SJ8kWq6AT7vGm7GQVIojRfRf2Rmtyj8mFVfoNH+gl1H1aJTkpys+mcJmOB4rreS4qAWPQJwAy3A2KTuS+geE09Py3D9dqIGiPKdWVEiFlxua0eOCHcyajadDSTZbmMM+K1i9k4Fz09DNs3gLwvd5xNyrbe9khz/vtUOmg810cbakwHxML/we2M+c8V3hKqCniZ13XKxBh8Nv9Z/F8DrEqRC1cEgeTEChyuBpmN3bnC/5YzbUiPtbAnAttuhwL+GCpMdNxXzqTq21kcxSVZut2AZjD/NBCmwdbUmzZSp3Y4ppfSkGKmosOqz3bmGdhlITdYOL5uMufQ3n04g2JsMKQBuXO6nBfJnoOMen3h8X82+bC0/67oBtTyduLEROQ+E0sDrG1PcORtZ+SIIKcfnFW2fpDIaKrAsXKZAg7j+N9XSLrcwCBBA8LgFeOmLvPXPA6ZtXDNKaC6vkYHmwpyPzRZzfujkmVmKM9yQxPvrl0jtyPe1Shi4SPhjGvNUXQVZM2IQfnEthFjFJDfj39dyovsQAtIb+bENAbGnzGKHdBeo38csm3Mv3tAl63BZtIpHyPfjTJqPo1xoN9J7ruF+4G3toaah4hQqju75lfwspT1fWTxpl+BC5mfAokSbPOOr9Yawzp9pv0nhT8KgNfynvkXOqpbyMgAxdgRujmr5lSaO4VRewmOlQpPkyvUgs7uMHYIR2zwjy5wioriJvmC5huCI6gnde38i3/xUSADTRSoGpTAcvPun9cq1PyN62s9zU9x2mqDORYWIIP3orIL8+X62dzZJ2gbnbIY43Cc0/0rq7VBdL3V8LS3f23PJdFMI/PqWfGa7t0bBM8UpFdMrlgZRlA8+sCOLAVnN9hYqvrqVcj7gVFQsdsz5lrGcAgHlCyPa8Mblm5wwHiM/Y8L1LH4rEcnTUlnrw8ZK+i9o779ibpDw+qnToJaTKS3y+iA1iaJpTidUlHQibrheUDOEGYElOflBESTYIZAi9cYgeRUZOhOH6O+RLoeGTcC86SjBujvaK+Y5MiB957RQvyJIHPsnVBpmnJ8j5rAF3R8L3fLGQmTZCu9X6RO9UKhrL/09HcuieX5vvW8bDIxvGYLTeV4UEWDWBp9IPiy280POh7OTMov1DeSD3BFgvUsNVW/0qoR31UfrUKAiXlb8ehuoRAjq6ScvMyy/VabKHTQ6mtb4Cf9EXSXbb3zyVbjgpNz1I3Ib1zUa7YkLRN8Bq6IxpeEQEuHJq8LYZzCsAyulHw7MGpttqbYhFzeWEkjt+egeNxNHtjamcoGHT/qiQXQkkUk50p3+vIT+2kZ2n/mmFHbyU39xtxuR+yitHpiUTbeTLuaGVz2asKYa5qCtbDiSsi1UKwkFvADnfViD1CCVwyOYSWZSXHY8am1SdlAagPPLO11UmiIvcyjBbXQ7/o+68TLH6xlBJeRKcO1ctDoxe2odrFCPe2gyxGOk1zQF9AixtOrTCk0p9+D0Qz3x1LB+MhmMuTnlsIdRTZHOu+ftdLS6zyyStwUU0oQuHSqeAp1uNFhgkp6fW+uBUndQc9aIwFPdWfEI2dn9/t+JJzuph82f6dmIsHc7vN0hVsNG7AVoEdUAi1M64m8c9AQWDrDGkhkPXtaPYAQfSqvZ16G3TPsmi9Uu+zyYf4bdnNSSjVRgA9EUc23KXaGXbWYfZCIiALirkIM1V9k/jG92emn/aViBHhL5rN3lcrOjuPRvK1CjtJKvqaN3QL8OAQVEYBnVzV+EIpYW0G6poXY/tdRG8OSBI6bQS7Y+mr+drPq1cTZrc58pMDmbbOPvLEuNdp+pdDbXFHNNb/n8Qoxxk8iJKwVsrUBzC7sQ0w7abaY5LBoCWFDA4qRWj1fhE2058PLT/VKkQEUVZ8KvuxcLP8f8Gjj4QchIMGqowHNKjSBOcqLt1Fzfq0IUBWRYHTg5i4VPj36qWBOBOE1EPW6ThaUVvXwGSOtRKOs9R36ozvTmXBgCZFClqGTf87/Qs4Ccnno8CelwjylNZf9Mc93iv8nQz+ICOXI6m1TvkX4lmWwv+N8KYb3E8PW2kHeaihayHjaQ781WO9EefsZzRvH3JbWGQRvFJt6LVHDRzK/WtxmhiCYruZhrdVLBHokDI6TszWNyo+2cV+ZvdEUrL0Xt9Ttxv8eXHA0RNMdfqlD8qOt0IJrwk634qt8OXxWwXdlHiUvF96XhRRBBfZAmJbw7y9Mtsf+Y8nvEuClSm9kP++7vcCSMph+RorFOBnjK1dftRGvzXr253SopgPwc6bXACVT0+g4P68+UUtFKUPl0ePqoM3OL1g3H/MGHhiQrnAC8yfwrikWisUkHmGvKcNH9G6odeG1H8IUgUPCmU+hqNPeAsZ3RlXffo+iny9mdcDU2Ka8VDm+yuzCd6e+FDOPArxdm2rL9U976oDbPjmx7m/WbSV0i2aTD7TTosmOPghFs5W3v3/jo7p7K1ulOF5NTmgCPriPDdWOnoj5BCvtIu1WLBfGs8zXAZkIQit51Zl3wVyv+m2KcPwtvmhg3DJTAVRedfDYy19Q1M0EIor882z/wOo8XzVwAr8kKO+yf5q6xPkHy0w5IUov9Z1745bFhHFMz/R7oUugZdx8POSOxNgEMBcx2HIlItgetSdjz22/hSo9rG2fqTvGRxXV0lGEOIq2Eq9lRtcntRlfjKnmo+Sg8R3WSUmkDAoIAFHoRphHzZcKgtXRWgv3JTOocyabLKG/0gtgX6sBB0NOYzy4+UW3W419XpGdkpDsZoEjYvVWiogZEi8kePSDhOM/GPWZrwAZcRqmch9x+WwW6qnFdhKQq0ki998Yk5MGP+GX5wODmSTbuIFC0coe7r5BgXswPiX+C78xQ+AsDgoKCA/HEIYIH+MUR1191mhhIgXfcXL2oh5pSkSmWOx2wvgCZ773MQQZZs0ecrUiTQJvf5wwTC9CeieJFzrObGOCvjIEDr2k0hc8jbHfaworOJXQT6hSitmzEK4VCRvSxOKAyE7gwGcXLAZEzVQCrHGLA3CPDFByGjEwW6M9ahOZKb5A/claasyyDddsIfRewYm/fNX3Jp3Oi4iQS1L3iW8RiYYqkDT5/T++psAXmlewsVVlVg6gLrQc0ySaz/n6l96f/yncwYSNqv87XdufVzkgcvJZS8haFxxBbqhwr2Nxh8uJUKdZtjeOCsS4+3Vz/vYU/MKEFOTgd3PWn2imaDrcLkbqZ9HsnRoxNLxTb8rcv/+5B8iqFYjNB/3WkHBIdSZC2Ai6FDXBeYHLGyS+H/zvJjV9KTvbkn4YGFum9oo35gUKYrx15ei5RTwo5+puimWnH1wLoetQumDwoBgZ5lCkABTcgL5iSLEcOk3Q7mBaqjr7Y5vkWHKud0hsN4csMnu/ZGQVJzYLM/R4dCzg9lgoBN8q6qSR35RUlEHgZXi5l8/3b43tV39qbdOfHkzi6L96CXPUhwpPyZmGjWzlDNYPbqpR32RYOp8WzlIveG+SgHVd1V5Zgof/Sj5jnRm3///8uZP862vbwjmNUw8cN/OZOG5hhjtsNnnMjSGbyu6eympNwq4jX8PjjrbupuPnAXmh/KaW1W6AGvOD55K2lxmGaWjeZ+wrhVku+Quz1tbyM8I5hIkHmC8UCj9Dw9gNouKmhcNKfdC/ou0krkXdvVjyofo3IYvq6DBTCf08cxInhk/6nQ55teDcdA0ydxEPuWQLqKyNpRtfuXmMOc8fuoO0rVv1o3+7k2qltpqHFU9dtDYh5X0AianOS3/rtk09i6BipLVgfHafRQZ6bket9DkMMw3GTGA7tOPYHKjMQb4/rTSPOjESv75MlX41RRYAJ+WQ4A2pq+wOO1vf4L/DlCks1WQYBbBko5AH+dZXPNi1It+V2ZvdxnmBTUE+PQVC5YY+gOFPShz+AwiRmpmvIShOOGt1AlM7/FV1yqE1gMX96Ev9I2Qakm33ibi0qssMUgxUSusPQYcHSmrUWfckDHYL2Gao8uem63EfISsJWVimm9itkncacJhfd4zPELzw5f0TdMlSDXU5YABgHB51bGtSbxSVEXMldDZaIce1oxWLDCR0N4HDSsNWt/DixzrfnVo+NF2XPUVUFrxEg7L12TwfhCVnhgvKfYkxyncbLl3Sy4TuZ2S5uWvKsiyxz8jszcIuVemvPV+sFdaFTxnKqmaqugzfj2A7K9puzFwhtkN+m/fvskCQl1yZBm2MsfvtY22pJo3aX3xpzFmK+pP2bW9B6p7XDmzBnEz+SXMHXUzcRd5sSay1YnvoMl2pK7ERKqpb7pnmAS4LoRTIeLwLBnGXUZBVIjUnmWxRFhJEPk0NLn5mbut9UrzbL0A0CS0EO85XW19K1wzpVarxNigiLWSmeWMjZr8jLPCxmcn+F1TRgDHQMMNxOsRG/I/+hVWsek94OJzYWmsXNihy6tF2Fx1urcZL+lImsTs3zjggpqqldCCstTcnMnY+kWlgXQi1OZT1+Z+fMEAlMx0j2sHjX0SB8TWTo0dflOGZndhoic0uL4PvwT9ypzEkTXqEL6dqW9zTSsoEo/tB/l3TdC680wa+FGBFq+OjfDbzlGpA3sHyOFErhpRTNg+MzksJNNcm4fKwESVpjLxLFQmcJNC+cRLaK78yJGbwKsdiOEtY9EA+wHDeLXT9trRxSf/lnoyZY2wXA6kSgd32OYTCiHaYguIxUfWDgRI07/KnbKPWSLdv7Et3zmOuxPr8JfJsH0KpmQNBOwcNOi0RepbFgAAAz9QZ7KRRUtf2k5vOKQ3rveRWAEyFw9QeGzGUZ878fcs8jV2qlZ8XUb48i9/LiHQTmBd4S6iaIEbTJqd3iUgtUBiEzIfRsjHOvy8lL0lWAQnyH7qkha5g0CeG/KeXniDasdbz5V7MVKy8T4mHIlJ5ARgqnB0/qwiPNnpEM39B65DzKSq+hLrBjyZZGUABwdDq4dmvPOrvns2N4FiY0VKDtLyUDisZ75INnFSQuYdF1HzKTmE27gpS2RolB5acJT2WnJYn/7aUGmNc77saK5riHBHdqq21LYe1UDCp86cGP9FPKLZxbtGWmlQYRL4k6PN2acZNgGqXtnb0hEomM6np/u5sD92fZMmF0gVhFzYq6WaOVZoJD/rzFRP05ipZUoZLSSp/8xzxoeKHsjWJ3gubrw8wVI1uKE+U0KBn0yIKD8SKkLXoU2B8YX5CgbOkbg04igeQm5ecN4FBKC2sbqYva8BayXx7aN0xApxG+XjL2sYRia0E4BSW3AFqlWIlOKgkSAF46gGSyN/UiusiuDA/kf5H6vYEzinNJSIT8fBgFudvkKGpPwINlbEhpB2fEKFRMPirgUgK+fy/wYoyNNX1nyDzqLhIVA4H/zlk+BclLNXQJ6yhys7Qy6U8hyIr8zjRhUK89IeWPlH4DXhv5VisKo1WRoo6iLOXgnx6c5ghw4RUFEw1BkD3zaPief1Lai6xRQBgM/HXm4us5n3OuFMd7bEsMr1kuR4+6Wx4jyDf6KkNq6zXJNMJRNPyPwACSAJEROQ10BpKjKivvvdgqhkOktIJpExB07Mhs7wNBHAImPoUba3XRuBtMufV0nm85DTjGNesuKzWU5X67kkSCYHLSm8KvgfcpxxRg8G0Jj5iDgAKDbtMxVb0dCZHgruj6qSsciXka9xcfGhy2bB03vXCDv7MYSJZ2wxWVNWdE565boPeYY1F7u0zd2fUCEUA7iMBC1oaQKuO3M9qEr8b0o5YylRZoEGPMQIcETJ6ZzIVhUNz6BDLSno9e4/D7bsIDHsA7Ec/4HJkO53C3jfyBAD/e/PDaehhOapVFLNK9bVXzJvW81Wi7DJqrpTjvDV1+XvNGbtYEJTx4/SIjvHyDsvvicu/U7Xi9rPeK5zopRiLl3h5TBsW225hLTVN42ev7bIrSuKSn/dtTG8nfd9XcYaGSr4gy8R08Fzs+oP9ElSF9ygudiBeePigcA0C5jkPq3Hn1L7pcZgwNEBahpSZgxW99D1wN/DdJp27I6AGENA4tt9p5eDjkZcQvLNVHP/oQd1cFu3rh6v4LC1WRtUsDebj/ySmOP8+XJTIg/KM4xGMhS0G6ra0BPw/9SjV/IB59X2VdCUJWtbNbhPpLPLJj+N1U04XErJApMhxv08AhQk22ueS6FMbrGSMSXCaYxhjj8DGz4ROs3bJinWVjMBNByIan53O0e/AsEc53puY9xJcOxx4XYhG8qEtp4hPsOqeXh+dluNlT/QVhijP+wYMaxN6QTxgIK1O0Kgk1XFjq1znkFv8SW0nKEBEjZdJ5IdRPFJ9GH8HsgNvWx87kNAdzCCBYEErGAE/kpN/P3+ZiiqE2KwisQ00qK1BWoLEAc1J4oRGjPeFlIwReP6ZhTivHCsegAmDaB+bVTl5hcfA6sJ037qbWqEq8/fSqvUHg4HuXAFak9kAwT70phZXoDEBr8DGicgGYiLhAwSTFRTD+hdyWHifsb6KFqBygHTSFFTfKoBmAyURhT4fG7RCLW8A6JdgyLfVJq4gtj7P/g50SyrQcHME7CU5xt4YZ1VJiundpVMtYAqaBRYwpWQM1xIa7VmotDL1ST1bHbXpH9zbf8LMALPTOTPEoSL1KsGgKcwZtZD/XndwwnIZ11+TcYyYeHPBq+eHt17gypuBP72Qzlvb5dYdsO/ArovlnNl1RN03Nw7rLN8PpkfM+XVh6yv+bcQn2cTZYxwK7bYU3Yj+4y/fc4T0uRaVv5WrRwbOxx6OlUuQgwpu0NXpM0+KWBRus0fBSgTFz0Jbvu+WRiOqNCKHRMQCFK8Yrfw9Yzy0ao5iJS0F1zWQqHnnFmoJK3frSSt1wn+NrjWl3g9Vc19YU+nizDL11hKvIrnNuB4+HRMeqL1yq+PshXA52rilNfNrtVh9E6Pf06oAlH8imSFl5QkQ+peZOmP97liQxSyek0yZUYOha3JvoaL4pJe+2IT/oRjCEBL6mK6OXtP4qbWMtkNs9W6R+HWge3RZ7vVDq4JEa/NjmiB+AjuGLQigb137RQ84G0Gmww8Ro11i8Xz4TdTwgi6yVHLBrhbfc+mDyk06UsAD1KavAPc/5nLUNiEztnMNle1+R6jZLoVWXMQFbqwDTSCD3K6cvwD+CttRsv0d6TOouB6HwAMCL576SYQwRXmRJ7HUyTrFRadHb/wSg/m0dYkhHkiUpQRC57wFjOTIXo6AF6bMXyj9SW0gyFmR43+vJP2l9qQLX4W/JzqXWAq42G7xs88c02OY2LBIV/N0Q22D5b8u5luspsCjsageUYWZmD3crGBcGi0wTOHPqNjI6Cc3px++H4EhnesrSQwvdKhHJX/Dqk/vgrlQctCxofUhDE3FGQzfG3XOxPns7c+k1+Vx7J1CuHZ1sBEZb6pkXZXvlsMGHgf+IcTWhqdiDG79rZJciPTOz9CRXe3Bsc/nPg1bVXwtqCDY+/xNktvCjjOEsAsr7VOXDLeYs0gpajdmBGYTS/aJ75ypc6lHHZBiFPLEx4KPgBr5yjOqu0w7XnhKLzJ3nzMwwdISODZo4RhphhPq6HmQ2J2Fkz7leqlLC6cTYMW15iOxMD29fkSwVCpHa7ee8k7byGsuiY2nKKPePlLWjZ1EU4U0mfxlUGCQwzP1aTPHoervr28Mw3689MeXVkTKWnqce5t5dNdLMJiUBFEdnZSdI/b/2stVOnQxEXdw4+RlGs40zvPouG7FUPjOYH7s/ERhShrLnqTF/A+apRMF13eSQBQgPEY7ThhRk9VrhvyU1wjknvlEuuM8bKsa38HVIXj4WR9kDoSUi8gLFEAP59rbsjtT+/QvUx1ZBetUA2vm43SJsoraCVHmfNWNDrQix0PbsRBjAv6bTbNdO/b59VeAO3EUlUhhp3BHTXyTpyS7Y3WpsMYrnoXLM8Pu/Jfhrr3mXeT79f+1KWw/aES+PMUF1X6a+IZZZJMoRj+rYXrVJTUgaSnmXgEXigG2KPOfsDy23M75ciFolBZfAnYC+NW4otl5eT4OL4CLfMdfQaA6oPieP7ZyhN1Y5KgrexcDLiYRKMsKowS189W+DMgiYOAqcv/uJ9JxFrYw9wn2O4rR6EXWNvyPqAqwrwAJQv/b5iRoBKPEeO3IAnBn9oHFgcyN66OH26Ciw8PMuL5Iutin1xs1SokDaMU3sg7d0Cg91F3cxBhRlUeqgylk+dbyIalz2UDAdEAPi7P/ZzcQ9TQMHfGAQ+nVNpDkvuJYM2P6W79WOSM8oH68IoMIfw2PX0iu/yIdKI6MjeffxHvlUHQixrFjBHRqiUtVHOaVt7pLoKOqft22gBq1FXpp7DXYw7RHk45DSjovVhSEwETOB1GT90pKZVFFnaY2X5xiREm+JYmtQBXX3lEWJR6v3lYpSo1N9HtntPZZVFPPuE0VkIk98bjSbnyNkoiHa7Ngw2sT4e+khT41oP1VYjR1EGdIOmZtb66IdYgcEEK5TIYAlUb6t4wRrWJfPX/kXKgs1VBFjeK9KKHSl12J6q6ZcvgmehFCWf29rHz3PhPlw/E1clgZqOGOojSe4UCi/ZBNTIo+fM4LD6TxoRYnCV6L93a8YXqEnBa/Q2X6PM7p02YKTcB9+J7nOHYrfXAJUuzQzbD0Go24P9VUYdloZ7rkGFNBGzDy00+xBNjuE5WdE8Zak+4JrMuF861KVVZ2eA2f4X/VQW8Ry20zzO7r7/2JOt22lXygJ/h3FNBJjZ2udHxBDSQCuRiJNG359fTueq8UqML7FR+ncb/6ZpRz5j0VJvb2y7IAjgulGbByN+DcoHk5RDEoqUoPUv7vqe3n//Qoaz+43EfWIrO8sdCDHskvoOqSWKvHSDCguwshXv7gtOWX5Mh+TIJ2AelHVnSnY4NrYQTcuALUsuJmJpCLRNjLxkBp4dOulUU0PfQd1rHsj0nDE+kqBjfhceGOMDqbULLNM2TUQ+8zXWJM9R3H/KfRPEZU1w2QumkVkrzWwDuRvhhSCTK5sG7SiO6BKMIGuf4DlNnk8nOqWGzNMp23SiWdd/rXWIhHg2WTC6X/GlMPQJmvPr1wIQMk6zVoVCDHfCMj3K5rcr2TFXqVnZIi/mLY6N415LTJlR81N3PLOYFp0ujOZgC33slfsgbpcbAf+NLIiMx+Fslm18y5WiuxrCQcbe4PXA003IXP2womB2BsQZzExQvnxyKFVNYELGmGRdonH1ivx7sgndpDSz9KBmoQAAB0gBnul0Tf934yY2GA6nrUl9IvNIwMuAkZs/xg+eCQZQteBg1ChkC85QlQxDOWJO53nqdd+y//ZlA+dNXYTfA9sTqApWhgD406T1diswEHCfsGZ6pHCunStKk3t5Jb/zQP01u6NR2md3okvRoMgGDnlPuTBPPNR5F3Ghv+XgqOVt/jVs7DOl6N3oLE0+unQnHD+McCGsFeTfIQmytov9+EKVRcc04XFSSR/TSHE7GzK88LpiwLPm9v4OStoWNZPtBCkOTa5JjLNN3xLKjux8rXL5hIrQb9VMLAvI2ppw7P/VXgB9igxCNPZ2/MseY6UD49bn/hofi+2FivCNNUmMMo5ADnQqrKMla9DPFf+6lpo1ydZRYK/1JNJMiMLYrYw7s6KgnWqWq1BOhiPjVZ/u+h69qfzl249N9x16vhzoIkiXCnXcE79qNYgM+vpWq7u3hT+HRFZ5vdV5/VCWSCUfOFXg2oiUO20Pi6h21Tqh9soRnmfg4lkh+ugZEfReKXBQGFBZkei01pWLunHO6k9AXt2rzwyNqbGcCyx8Q7D+/5SzG0m90sTT6lwWoRg9vRrhgL9PRlStqugHaFM6+/TrQrF23pm0ufGdVuMNU9VWUNq/d4m8bOONXJ/kuUzWNr6+Y5mvm6azj/E2R7xjp+0eF6W/NWXlHUUrsux4M71IKZPV2Oo7clWwKUSoszwicY4r3INEJsfTMU+togVtOE1cT2BacTpBHHABPKfRRa0fcV34bKiEa+afCL+d/mz4KOz5lOPfkFWhxmzduSBRZEeOSX2a9XN171zEcREQ98iJuwF4cOg29onWum5WRxmkR9j6Ix7QbgGrjYN6AoK35A4ctDjvpugy8S5z7z8+99YH09yeGnQlYmlm83TrsVkO6/nVa+l5WDmBg5w2pm6+5ZL273DiVVNb1vEQG9wvbdojhPX8AIl7pAc967Nmh29hNysvPP6bulry2zHClTOtZxBPsir7FJiZrT4fXZXHfvWQj1B6YXOK652GFcDnkoMu3L0leclzKu3j2fAB+nMPzO0iUU2OHQKXLvNX2Y2FBjjMpRpNVQkRkY8LNVwJEuGN25eEUNWdi8jTX/nwIHjuZ2+Kl/vgEWpIpsBay7v4eC7vkLbkQrVU1qFQ2ICu5LFI9xSMSuBezS0+SY/y2UNUq6oLXZBA4+T8G5UGi6xPKcC+lK7DE4g8rgAoIH2JS8G4UkR5Z8L//S1De658+Tr4N5YbjP/+VWxkEn+QIw1cHU3F/3Cs5yWo9qs6ow2FniCoTPYMuAySAvfrp95j6rJz1KjMXmSfnWWQZtqJJI/+K/PIN2PFWR6SA3Y8UlmYERcZ85RpEt56OIyaZMdsm6gxPQ2CwGr6Dv7RgOxlWwABobV0+l41b4Jue+oIIOnGeycM/aOuDbKJczDTWHoOoXEkzOg2K95gSSUpA2kc44DQb6cNlpt7jwkSRcVlTvtFxe8qkUkhxNKcqYvODJ72AZ6SudOhzgUsn1Joq4SnI57dew9IqvEmaovjFy6AB1vYi1HHxLbxOCXnpRamE9wbCo2wcszwhMdfW2Y3k6KJ1YvsRP+CWt81CquAF5VivZMhh/Pa3agL4OxRb7fg8qfqUDFBI7Ib06nPrY6SM9CBxtu2mcRKV2hHIR5HW7fdH/NuJLn+aaLqPNaKNewd1KjgSoXI53ZjQqYx1i5L+0jfZiqI3Z2bGDOC9mFLYYEN32wFrvSl4utqHadPqH9482mFqWMy2zgDZ0ZtqfKhXtY5L6enrOqV6B5SqJpd/eo7S3mzyjIccXBwEYJuU8doa2Gxb/MgjoTYZqKwRmo+xe5HYbSy/Rzr72Z8aF/Ar6b7DYINxqOR7Oq9M8VIiB1esYl7kui13JAgknshlSY0lVG6+oEijUm2cJQ3+IdydZxSN7wvLXgwTLiBMdF8bvhTqz311l90gX0iGcF5u9BYyIXHLQ+5vSTdfY13BFtVRDi1fDv0AlwKGbm84Z/gTUJkWyH6azndn9tEmgjqvLpTuQv9k+N+xH1Ez///OtCLdY06fHKH7fFL/g8hh3ZL81cyrbHIZpGJHKtmjYrYWTRLMzvi7f5YQVjiCAoznLQiIhZPhxC8uWNUwV1Dypr30B0iNicSt9v5/awis6DFOItp1+pfNxMNWCpjAACzPI3F4roSZkCUuu3dxV1LGWTOKUwXXeS0Fg9+OD1SmJuo9F4th6yOkn4UpKOCsD7XF0iC8yeZ9hT4ZPftM9SyJgcHR0bA0JUeHxOgLnbFvhtKP3e6H29UxhbC1F/URjMi+Cvo7Z9PvnreapFV6VAyPOh/A7uQ4G19htb6XF62iPocjuaWC2ueLczcPMWMOaQhGvn12Cvxb7vOzPxxGGUvQmQzdfNEiWl4tu8ob+QagDnecb9m/CH09KFMysW9ptk5uSOnGRuhROVqamXdKnCwIBx8wnbzT2g7Uk+vPicDzLYrwXb0yW14OrY79jdNhHrPaSdvpLBeG98WAAAHdwGe62pN/3AVTYWMz7bV6Vz72HYErPEOzreMa7odIRi5rKOPKn8PXDkriDLjBW52NkHWAZQhzon2ioC2Qxr1l/6amoQEqTO/lRjYUpnG28ZJvNWgQNrItYPfyscysYsCLGjAVie+4hoKhn7LII6Q40sTva3JqWMIOT0qYcUXeMYvsacRz2U6bapuvLRo+ZrgLul2QP753DTDPEh08fRUyC9XEV73AIC51kfsud+NeHRFnYQnSeUn+qgZ/uICjtMBg/wVjerqQVNc5TK7yinHcoySchwixF34Xzq1No9tlLqunKntn+ZqOFnJpyeQe0FxPyA4Ot8AVztECW6kKN6X96mS9I0X1J25Oa073Rf77ZvAZZo44GRyQVsN76AbPvMUsSLuDaF7mibnb3KkDe9pTRv1HhDkPpPjJ7p3AWD7mKASTajY3l8jP3nzIKLJwVmYhU1TuW+AyIjz14ZoMHt825xNGt9GJ3/EGFgm1eGejpSB/iLJJ/SMcF9Yc4OazYW7AiqoXrgMkRUO/1uL8EbIlRmAoMRs7jA36rLA3OwdzTi9SofJ3892+9zUoJSH4RYgWC2UOOpk3y9iKqMfncBvGb9PmkG+Rd26lQnZvfGk8Bt+j+YYgLmrP4jCjnGiYuj6ZGhKYcrgG6T0JE4uRW3MQDlSMpAlwSYWw8ETcWrR+FZQB6ZLyV1+12GjLI5FWcswOk/MfMfJRTsvVSYTrvvzDT6Ifhp+TcJC6SlO0QLPqDJ7mWeuwMxq1d+mpnoe8/zHC3wbqNXfq2fcLIBFm+cosuJ4OZrUU+wHOJG5c5YGqoKq1nJ0OYe0LAghTJN6zQHSW+QYADDw72qQDXLvWnMHgkF/k7S/UGAiq+quMKEzmD5Miljygv0lC9uc1aG5QYpUP1tJ1QexBOjYn28EoLhgZq/eFGqajF9CwejXSBU/Mfu90XvmojKKfKZdg4cA2UBHjJIqsMV9B5Efexd4ao7YMpVOjT6pqkBW0WuVuIsnSS0oKBS0n6Krl9cvpYdGz1YqCfFDzP60FKTGFpFoGKqPZaXobMdeAAxr//eLtyGpJYy2qUdAhvmNFYGXH3HUeqiutLhDeSxNBslj9RuSVylB/2oO1qPR7UN8rpf1LZcmQaogapQg7IVyiR5Slxfx468PSo50Hcygzd9yKc4a30K/RrehjjwKXbFguMQ9W0vAlOCPMz0CjzjpeoWagDr14A++JFbKlB+Igze3jpSGXtWZQylVZGmQ9sR2AmW8HvUrALvkKbiWT+Y6cDOLWql19LwlycRxPfoG3Vwbe/qsx5Ru6aleRbE9qeYieOfIV+GPdxW1mn6o40IVc8TnK7/oZaVkX7IsLZX5RneZhJvOyLollXUvdj66kLReBjwyadPNlpLv+PTyDZ4YsU4K5EsxU5GZDWebO+4d+i5O9G9dTSp1sRG+MkNeWlqMDk5ZVdZFRE9014mujvVfdp/P8U85XPJF/q1pjcDdfafihzLzN6wl4t/ZWO8yYjV82vX6xWZdrYPwV1fDrkxfk8hKBhpc9YRQfSo98Uc7J0ngqvOMUV7gCcKGLgZCpDYD7rN2X33zPztGyGel61Y0lePJZuWMV8hjUf4S/WDlFxVWeqxHa8L3/Ywdp1X3srALZH1AUHF1DBg33nESvlkOkTmYHV8qqraRXbKyp6ubRcVSXLWC1VgWl7MD7D5il6ujeINd4EzDyTcUAL4+GtzAJdwJn0ju/YbTsFUFFDOigYPFKBXgHQKXWupaCT9S+A5i8Zs4dyUxHh8Y8+M2DYLI2DXK4Ku8jKbBSMX568JGyq9CZqPQo/SEWcVEzoPGY3SAbPjiu+atubcEGxGqCSKjH+v6aZaw83su2IBCrEZdpqDEusHkDeZKNW+GMT2QMuwPfUWklX6W2x703AfGPse5x0xYlruW84Oq2K8qGYBd84KSjg09Cl5U+QaHWEbOh3+4aP4nlLxUj2T2CIjmj00bxDaJ2MdM2kVOQiero5ol7iBjbCJvz5h9LY536j2W1iCduGN1m6FXkch0bsht9KLRo2ia+y4AUkqwBIUMRGhKy4I8Q6IXEW3sjliGk5eGJWZBpNyBw9cZgAspfgyj48N3pCj8NFRP2ooflytnQ8pW75ESFhYHX4Y4Ze0QJzOWhaQQId+GyEgJghfDgBTW3myACHp7I9LbO8Rab2Lgrqt+s5BbJwec8eDH4xIlmQC6b6WaF2tODld8MwAb+9bxkHTCFRh6H7rOlbumOyAJBjnjH+lsQpLoE6uq3ZY/AdcO7G8VGiGX3/xfkl6KQBSsHtM0G54w+cJt+gfsxMYUPCECKjfyG+aHKdXoqfKN8olw62UwlfHgNW7zoNDwx4HTxLMY57CYV1ZPZusQWXJxCVFybUldnQUHZJ03M0MbO9MAKbqREdErdaRWcdXboDBVTKn1g4P5HN3ZvPv0byOb9rHKJDdWWrVJaniaZUv2jSYVdN6UcOsAsiBD+abHc9ytOzLTs0cHCnXeflyWMbG21Skq5aehRD+xOSYsHKtZ3MiBNBikBhvlkzd/NaeCINUZ7ZRWaAAAFD5BmvBJqEFsmUwJ/wFSx5lXrRoE+/rzvuZ9aTPNqna3JeJgaGDqrGL1DyOZoB/1voNjCvn/MzQQ8qXoJK8iIUKHDSjBeygYRVIPLUFR8wvVYsTFRYJwJdMdmiMUJ5TUgcJ8XEr2+twW9ggKlFWzq8Y4JtOXjaY9m9X1w6WYOnCa+puo+ms6/uCo2SC9UEVyH4Qp1CsGg/OQUyhZZkUJUV5XVvnRKW1ebawlL0cZxFnT2k3qacjZqi3tYwRgspNxbsolUwOEXy0pY7kx4DRSVkjNilJMUjyp25tenu8Q4tx5rfwrbNbBATazc3wrVy1j4hSOOMfYjnd+yuTRO5VQfNYGTMlo5SXJXSfHZ0433nFmvH0xKy+ZLAmHNSdR8eU2hQQV9WjnMGe7theyIQn0EOx+KgjnuJPVwjF47VswT8ODGNdMQq3rVBsHZdKRX4RaQL/Iz8RB6oXXoqB5GY2d4Dl5EdhIGtAmvMjInDciS8kzqyT7IZUYkB1Erm924+MejLdZT8xOvXsbOIUF2fCC7fst///ljS4/uGvVsQJ6BvNsuFBnMqiQHW//YAxfAzpccM//RFAhmOeTkt+ebLyI8El3AZ9ZewRn8QUGsKwseRH4Bg7xrr1TVESa5DVR5GhCAShxHtDbF0MQAFMbxxT6iar7BnWFT7M8OxPEKxx8BN++vfi/Enjs7FP4EH6VYhKQZyPB/5DqS5SCiKmxEEK+3VTLtWAHUodfN4v01SzwprMQC/FKY31t5P1oT8TQ6eEt+qY6AiCG9wjl7CCGcbVvs1JSDAAcj6EyYJR1h++i0GAq3AtlzRD99O3bQBP116VurAy8edzh/ks8ydUniuN5+NN4Z+6u5+pnyJWceDE4KLkt8ZHM2qA4Wa5IA9qu10KuVwKptEWRBOF+hOdGKlT6DzxyPkJFDhTXMp5K1tGmQrVptAkU3gQidv2ecpvAI1eQnnQMzmscQaVsa3TR9DhPq1gOIGFujXZYWGnvkOa6WKPvos67efE14FDaOjUGEaYu7ic+Cgo4+rtpVNzfWYdGep/+HUnkAvLU040ZHp2fNLsBSTfZm4QAimVwsp8b2Vmq1Y66OjWsP4NKIjJoFxLw+nFWSEhGw+I33QenQ1Q/ZSNfIYL0fRsu94wDyKgDElT660Vqwy4riLH9JRoPddVqdbz3sYyvzcKSJBkKEkkMqi57wRJMafmyi8oCmZClu1bmQZoneL/pnY3yShxATRbiZiR24a0cvb6YVfTV4A9O5puEijoEUtMf2g3Hr3NmUMGkWh5tpjidIgeDWCB6EAyQwopSBn0mAtjAGaMIVQlOXMYCIOGiaJlUPfKp/tJjVLVxcoebYgCyP82VFzZhEql9yufep45FZjKRi1smo4MTxqmKXd9FBdv1/iqxboCLse+DWNhdFsYUUl9Yn4PIvJi++KGJ53EJcUU6Qem4FaqiiAqQFbS5PvZfnkm74K020Vl2P6I+iXQgHsFfdXaF5+NElx9MJYYzEUHbKjAsbGTX2A3QfFvvjCbZ4Fo3NkFjMBDAAZIDbv2y9g4G07MQm/VmtJBQ4BMCc3PdrEMU9kyn+GszX2YPNpXvZgYj4PDleOkrSxP/uV9LoukEjli0SpHiRmJpt/DELiAMAG1Salp99yPag6MSjo4p0jyxpJUnuQGXKdPjFjadNbqTXeqfoITsGwOgOH1ieOYWPZOInxSV36kb4qrOeDXGGJA0jnFRnV7ccAlN53lmBfe9LKCSKLVGMEZJZmz00qCFExLwaVjaGc2yx+ejnCyvS61P7qmK2c/zJrXimdzHqkduqgaIDJNiz/yIKAyWXJBr52qTEQ/dQRiYIBN/IU9Vwc5nLKtNJYGcT44v1aBKWr7dxZHbvsxOWXjTWE0FR8GnC46PlRG1SDUz0jc8Zrueh4hTChtY/8tBLC9RtEp34NliOn9EBQckZ129/BBdQMl7AFUX0Pu12o2bLqakQdyQewFGdtO6jnOWMObC14L6BbNuN3TcAPwq9W6x6XZ5aLuDvmmYGdWsMgL7R0xrRotYXHKgsVDeSJQAlAbrbkZBayLGFvINF/4whSjleZnjwypiopGpuv/7siT5fJ4UJdbWguKj9SOwwsg1hhIaNNAJcJcMxJbyn4pZlkZ213NB+kF0i5WKqrXluAc8KFuuArvWVJWuSc1HoWYVFL26TRW2u63jw3IXnfgQLtU2Er2n/abqBMNskkYJJb0YHEKqRhpDPIdgvg6Xjr3kgrho9gUMOZI1GzTsqPzNjcH2V/4Ag1GGOpgVnI7EMy0hh4EOcL2J91DRFuSqSyj20JaJ5zcWRvQGgeotya++XST3lljoD/Amp/9Uebdl5e2m8zFieIKx/0Xtvm4C9WSh3Qkjimttefl8zDotvNCUrtpipLTIKWq+l63dp2ifahNgkiDqMkZh96JIhhdLobfq0pX2aTaJWABypb1Qd+SvR9/D9HG2cGt0NX3pq6U/3Sxb3+dtfwdCwqeU0rAAhmS65YU9pRu/5f54hv5H10TmsnONcQLtncgC4GMOD6mch9T04F+V/Lo+0hIlbNtbMG3qkrn8I4OWATDDaNz+RcwGughkQDpwoeF6jE0LVKXCpX6PwvY7li5YK6PL9b1xCxfpqqJN5We3co1S5IcQY5jdRPruhCGKKLykJNtzsEYyAYQb2Z/zl/ZPf+ZFnMT5MOB+W5+1ARO/ne5Sepqps72WSOVcE0h3L6aFaXT1xkF87pFTYXz52Z+vzdR53eiUgDEZJL+zLovWFuAzL6ogWtIJ61JmvnC21FaMVU6a+P8Fm769Ue90sayn7UYAYhJHBn/Ge05qMkC3ixzkKHfpVCuUvfv5jASQSE7TZRZbTD95jnGybcXPwkkPvBkQBuZPqJLK7/T13WzoTuYAgT2MsUBLt2h5Lito/gNylSlQp+SIngwEGz4fiVcCPH66ZxV9Qo8tNCV7SCF+q2MEWtR0siI3C8Th9Z/uwz86EDjUGjrVi5Tf0qtHImlIoC/QB9tVBXeamIpdJEaNaei03vGwO/cKHidhB2U72dIFQL1bZeo5KoZR91nbNsoL4dlrH8mK3DJmj/XGqOlfzYkJ4aXmvD293Fl8ycfEBC833Bj8J1L97ZETgZBW3hdq8RpqBgDOJO88ti8U9rfGvviW4hKUU6somr1v6SeyJj8jhkrlE+56Um5VHAQADM5u9Ej8t+ovt45kkOZ9Gwlb1L5TNyS/3M1SSh/IWezJObUW4r4dxkbB1gLd3QvEPm9Ed89Y9VGdaOWTQqcLSiOW1uaKqRSEKESUosGtwb6JlTTjv/G0UTTnBunN8VKkELNevtx96i4VKmqmVEQhWuKIi5JZbYuaUwVzKT8zz5Ne3feSBCKvD3CEhYY5tYlt7wlIX0Veg5Dmf547fZzzHqQu7PBipWiG3FlXWDGofkWyvMyolGrlSM+MepU6xvY3mXXd9rMK/+6ehK42s8jhJ3TPX3uK4RnZ/mdbzt2u2y10XV/1IR+PtYcm2KO4txDto+zHbNHL+bySNweMnfiLk/7cyfi95OCgEAfG+/Rgo/HT3RiovQ8yS8JJV5powqDCs4FFLZ9CRa2NPrB5UL91JN//YyKIBtLeWQQo9IecSvzn9HnE1oyXuEHU4KinilSsWfUl2W/d3nz8UcEy/AKaF4z39TOQE1ZDv+SKJelcygpBvjB/HjvZi5L3++ua/wzcPe3I/XBHa5rT4BNDorouwUuDOK2zvlod7qOlDwZ7Eep0/nZwvwPNRD4oDQSDzqy84SqRqwv1AienIm3TEa6lrmKFPEE+SV1Vuw8gVa+iQ01KqgdY7s02sFczsUQrB+LBnCfcMMJTb5eNu6/u4u0TZxAaG5W6E5gZt1cMonhM0aAXD/ZIp5xlXAeL4PBUZXTKwtC06YtLKOo8oLhj25D3ZWJXMgrRu7+kWSHZBAlANmh8Xb2gN9SifeQ7Lcc+hHHDcSvgqOcCOyyhvQwrnMEXwxbkZD9XtZ5xbfxpR2E3QPdOhVvYVk/aDL6iuUzuyJ/Mri//qI/aKqhZj2GqesY5dsmKRvctFMnXjCsd9QOWukbUK+roMrR3Q7ZUPYmj77A9fJdEMOFRyUvAwkJenLSRDM0Ar8CqkYe/aA/VIMxwon9W7ZSU2qSJW/t4xoXUzikqQ2X6bOJaiHDpukL+m4KqHp48+XDr9XzQG+O6BkUQ3LR5A8JDuVknACrIQLUrXmJ9yVvNFlZ8Y1hyI1maUAsPybZih+a1BSpqCkL85ie281BgrBsQhkTOUt4b8UXOvhiMFjP2ZDOHYZi2XBM4YiEd7NZE/iwU3TxddLJPFgQN5zzvS6Mxi1f2wwAVVwAJAl4iw3rqcKzTCZCwi+hPEfURa7MCZvsi11eO/RdZOtdbABN3FLvCZeu/E/G/2QKCXSBWjye4LBuKPy0QYhdgXgNlk2A9Vq110tmsJhAo+kXZGO8VyHRbKGsLGFutv20J6gZVFhSx36Wm+UHNXidrmWC66cB037vqk7ZiolNa/wKs37+lwB2iOR93360Tp7e9S9/unebJXouLR+QrMlGCZQwrMk7oiTdXOgpqqgLLYoGDLGMlOFW0SudL6ff8P+p5rZRh9ScoRcPD/GVr6YFzc7cX6fasNc9XVC6THT+OyIJgcNBKiBWhhtXPJ9vXY91sl3XWezbWMb85+2FLe4VWpF+GdMdk/Z/RWu/6OEt678vxi83i2daScCtM2abc+pl4EAAOX28B2M0Ts3R1mwt0o7L9uRjeFaGGGiSJ3RWl4zQ5nQmUc3dtO0M3u3VQ+qaHUTdo74N8vYpr6aZhhh+5R9viL6/vbSvJ6AgNUJerAS+aVT6016UdqcEcpD6RSM8/0JpuylWnmSnx5IFRelwusq3RleRg+l77H1sGQvdc6TlQMy5VqybVkI2tWiQkV4VgTPVpU4mF39eB9FZOLjJE3negpMEIHLtPXPhK0d8jlDJJ6VNlp0JYgJzIGRzW2ifoU1qp9xUpiuVdzEJOY5hLkY1ddbJvnZ0AfO4ID/mjEQ+xluv/qyfTC33xM/MvVecHTnA60Kmq44/F4+8no12wg/kMbjvYaJvXKraBC98oqxTVSthUgGUAIscX2EH6+AR1ZaHg5jPqol2oUlmE+Me/xHczUwoI5gtJo0nLzkscQ082pWJtqQ4ovX3B0X3Pi0HhnaNHeVUV0+EzDZqkYaKQCEfu/LqzannjNphLqHLZFN8qF41IPvFjORBt+ZAwNZmNjpqCRHSfvZ1Jpig9FuucMrVKAZcr4FNwKU6Q7/WBOWZCzHqyHWJWVkS5sVGYZPvuo6NWZjR3cJMnN64fRwQh1tle3ZlO9pdDVgpCT80KYWexABn62+zNJS72pwPbPA8+IuiXb/64r5cUDe+37qAB74KCBbe5YyBOMNikjutOV1LDfyGXUy0kAg7tlCsRExPJf/56EfG178A7KyNUCUCesom+jKWz3sXphRt7sl7fulp6lpFqHh6JjowXH7qFm9ai/Z0mp6vrntxtPWQldm4oj80MKEhJuF5pc5OOBJwqqEcT1qswZgzcPJlAe01DxL7LYaI1f5aOg8YTHEG+snu42dC2gBuTeHNEnAv5RV9RNr4VW8wYtVL06DO2kMl8+8EXvrebP01s7c8OmKdRkFHR0iCp1QiXq6Q8jKzdQU+pmxA6jAmlKv7jyv88GLhiR2mfXD2bi4zUEpYDBuKAxX5U5ZZDExsxvFH/Vd3/r+g1upEcNtHuGo7p0vXQoHzyr63nfEZsMm0bfYMpa/lBOX+2rGaVvNzFa+8XEkR5C8Qsf8xgCaRUqt4b1HhMerNje++ztjbUFgaoA4H3LbIgZKML5DkpiBADGkzs9chwb1L5pOYUxvcP1zVwFc8/1kng4NJ5XCZ/UPIGU957noYzReLQ/m8+gCV3tyG71/rFqg9JkAm0oW1e65qyy7G8vbOQ7KlWbsmkYbzsqL1nCdZ1rsKBxuLjNuV81nPphBDfadJ0B2LCQ6UR/ISlw8DgPk+OKk6kJglkEHuANeNLZs2QuH8AlXhkOFl1iqw5BDsjFqTyKh8o+OJ53sfzx2GyjlEri4h86P4IWog3KlGkoFrI82dsPLrmZ+srn+PW9kkh8XUR1mvSfZt82yiZ2kPDZ9FHrMY4HyQAFhj21kicx1czv3UWavY9yxYwaCxngmoOCljD9yK3AA0np4O3WJ0yBWk3Mntg2MwZeSv7RTbnGfo6x5pzrfHHuL7J6Mk17jB8VH6DzVurmeOZojqiZhaOC3EPdZWmBlt5wHHvMt0qktWFtTuH7l0RV1e1uhvfvzRjE9YABYS52bX8GpD0cJUjS+nTZfgr5hL4NJ/OLLpPqSTS88aAhVJTjXrYaCv8TUXadN1f53+0weGxUySswKh05mrYoIWR+n895N0rtMELrqhbgx+nURCsCAxnGdfMsRnvaInpRvPNA++2DOIBDmSvnEs1WsGZ3zMHlwYAlRee6cpy9fBSMoJcPrXSGF8kTImBI8IlfENLObMumoyoc+5oAuKpFkaXL4BKqdZEcck25CPXHH2dumWRGzN3sZ24jxqKJkUPWQ1qTUcPdGjxvUAamBAnUbJyYmhI2LF57WW8yFnYrqbDSIaWd0irznRHSFrZKQetUCDIpCnIfIeWi2crKhJC3RU9Tw05CCMSJuPxHOS+Ua1jwpNYwTnFaCyOJkd4nZg87EYjC0rdwG4Ou3ye44fxhpbSBTrapkzqLIBVxgh9Z30q+pnVIoCVXBXUvBg9lptuIHJhJoDDlubX/6AnINPk1zzGYzz3Y4oACL+4u1jAomGhK9iqeMfn4+h3jRFmYU4V/fVqCKLRgtSZ3t1TR5ufo2Tf8tJsjzUl2YhVDl7mdyBK3PKAJdX8I6UwcBaRypAhqBhytrsr1kQ8jpnYsB01L0Q1ba8Hm4S13Q8/YNioR8qbAAAK+EGfDkUVLX9lbULropGflMYtXs3iljPx/xBR9Q99Z6rPrMb0bnV/Cj0IYH5V4YrPeVrNcEU6FHFnHTPmIBs8b2NbY0a6bVKpNkBrT6zCVc++RMbMxi/2h32AaiJd+qNf27B1+dwKYULl74vrkJhf0pVwPhD8hJ2seJlUnHIRwJhmJR1pq9WNuEMxLth0ARZ7PFh6RwtC1A8roM7Wv6uMdN/KHan+RAMk+gidw+ju28aNw0rFc6hFTkrDfHzXZ+bM2fCIIJEywn+tRGAY8nzhYsodUzSFeVGR4u+1cPnneov1BLPIuijOHAwwJDCUAtwKnPmU9sjGUfLv1QT7jrP/a3Q0Hp7zaEwckS4saXSFlg+AMfTcorL3VpFkDGxlh9uoqlDPmesCfYFaUT5Eg9FqqylKv2j0MQCXtc6ODeDUmcJBqrlREn4Qwvf143ire6yqlGAS/0pvHc6u2+p47qftw88qrjwAnOSYdw1ao9e0o24HNjbJh3i3cqfEoPqag0Fg5X0kipWnRWFyKwYLmMTPzmQAMTQFWD9/v7u8MdumybW2DWt9Wz31bEJxDzKUW0NR6FYkWPKoA4TLXbSl9pbQszmGepDNqGHDV+740RyVmPq8QdHtzCJfV6RpJ7RZ6auMMdHdOF7A6kIdZuTYR0UlNfvcPK2NTOwjA+A0CMLhud1F2GumXLdPDP1ZEiVEjv0ekItB5AtwMZRMUf92YwVumDMF7x1p6qKt7QXGPSkg+GgbsqIbJy2dZ7bFIQ3WH+AgvBMxRDX+i8crpNHWwUOSZOtqJlz+jjx3/w/Qlr7BcmNAC3a+gAF/fFK6rz96fLYhZiENEIa30Lt1KKFmzQtrrNDo9GVUPVx0PpC25hqqBctdIta5/c+r4BepKyDi+TTb/83m/TvN1zjuTCjvS2HNC6XAFl4GxuX6T+AeBiKTjQGuueNNNiit4FJJjMrd5mU2SEyi7koEg2kIym3TqnLw1buKgAAS36nPoYm3FixUY0hr+LHU+BHGRLjRTmU675Gnd7LKFL1VLRs9YZ+4Blfs4UxeAf2U5D9UrEhMDAiyLtmw+US4Sco4677PamoZTkL0V7sPo54EQWK1T5wb8uwWfVD5OBE5bJt/r5lIJkm/5i+vWxQ7u2AmckseoLzJdlguQYRmzbxDLJKZsBhjNZdiwr3eIxI4yMi72hUdvbJRsaEDHGCzWtUvJWrCR81JBomTUsW9nosBUzrD94bZSIiCRHqhDHlZJ3rV32kmWrdO0IC4KBXDVFttyyPelD95KSA05l76k24zFd1/EiTtoWEdHM4Osta5MTP4PS1U/QbmNjtRf1y9IubxZ1nM2BdVe/ckQIrhhR3GpT1I1dg1HPuqBjjFRsL4nIB/cERYSjBbKr3EZodi+q7JpfPhm+vtDEPsY97nW/nJcFhTK871vWxZOS/EtU9tLY0WE1FYNnamKky0KxVxIyalJ+M10othbO/dHtvivzT+HfkYr1kRSHKAJwECX5Q4Z5nYxdrj7HoJzZNqfhvFIg5M1TvLlUuxVIulYC0Bl2TjtwBasERqIvAEbZQf6DeaSPgti4PQgXAsvJhTkWWLlwZCTeNqPI+0PuuSOo632L0Eb/R32STm3g8tlqv8UwRIvf+JtZvm8hox2xwktfhkNqH14bmJuZMvvgZ498xj+kl8SnvLRMYBfrcK9yzLBly+/DayhGZYx2Hg4Co8OCwgvpkORk4SfNxnDzDxEmNOrtABb9422xIYbo90bkPwOXmce8S1HN/VKcH+liCdDOs5x+43Wq6A6ezW1f9MqZaA/2a9ZFwtOWXBVCytxu+E7iMNEIXW+E23Jq3ytA2l3EQL1aza+GC7yD4yJOX7oBIQiklIJhtaZnhqlNZ+pwn533qh1jG6r0nNveFerDMehLikpVrLFF7uQqE+/HNF/cPFjIyD8iiYdtWmb7GQeRmhgfgvAaCbeNVzQJPPMvj3e7y4dDKuy7fo9wyZOBSwyqW77oMPjjNdDwuyoH6K+ryL78rwa1DrLG4+/4rzsxgyfpV0ieA0+61iPU/6owL1iT2feGN022sz5HodqVbbWqse4WYrwVBGLZN6s3Vy+dHpwZOn9xUovvW6NaPoCofv5Y88DB/bYcamX+ZHp0xJUT786KjNBYRS7eVMlAoHTswYrQZW/Oofcx61xxukJmYPnGtGXBZSjasTjxTgRuK5P4PV6hjD4rEcYcTc3vn+3xwRLtzVe5Ko6drf/JwnTfJ1dNHMFTRvrgwhsP/+p6usXx8ZXlWE4xao2LUCTmvj5cKmLzM4MfzwxFkZsuP7afAol5ZA7uQjwadkn4xLpXa/UCtbk5q4ZgCJaq7kw69kH/BB3aDExLynMc2a0h3xw2A7BayvhK7/Ict4hJfv1n+V+QiiGdcZPyJleM1EYywL+PLHO2LTSFTTYWCFnzNF0wTlMAjle7XiY4SRKu1XC8ZeQ2Dcx8uTcgxGqbNI+nS7pF4iC3el2SxMq2zL2rTpbxDkAhYzPOlDz3qnc9QJpGh7ilBl/K6BjnGAGjKGKU4BmIrC0CvwMkWzYLSFtgeBGAMpoeaSDUimBFb8VaOF4D+TwHbkDn+DAsnimCEAUDoLup0HMMFPixNlUmToeGiJpea6f+oJO0WLLsHubiNiHFruUV+0Ts2BKGG2CSfgnWYIjkFkpU1Qbj8dzhhQUHW8cmdEnC+Mcm81sxXXUvzOZ7PZQsp05VuQJITLWfAh7bH/6lteuTZ//SAbLISeSrFBwi6BJU8yWLX6jzG3S6hL2OSf5pVpvzBMXUhZcNNWxtMcxRd2lCse7Tnh4MlaNWwuZa8aMQ1oVmcJn5/1Hcn18bYjVFlfQsXzgk3HC0z9i0jq5wkPaQWTWRYmZTgWJRwrsLAwzBj3NYsh8K/BrJTRZ9yLwMkb63BCrw4eRkkXLAsT5AxT6ghGwsgdO1AM+uB8dUtyTUf+D1J38ataO/V5z8HNU/VRGQVl2nWRrsYYdR0j6dxZRRPVccmB61SWOvQ4ALt3LuzyTEnzVaH7Vn74UICv9rUT53w1aQSqzmPP0B1EE7n7ecqNdE4DzOC8YW+2ijDAKoykOk6GdzN7D9W2kwNbzUWitOxDF81T3bCcLfIT5mDZkKKitLgYyaHAXyWmBDQ8uSzbp3Fvq3NwRDVjSnN3edJmL/i4g3hfZz71HcOJhV/ineLBIZaJyecCxt2oO6HdNC4bPs7zEccnez3Fm59JO91Zx6t62Uez0A17gN3UAJiy3AsoiodBgVv0AKSaKYqs1tAEosNg9MZidsdYB1P7lunfBp5sACd53g6/ANXWCI8buigqhbDhkZXbSdFPGKxpQJGb5EO7kXcpWtw4A6jsGtCXWTuulMMa2dQtqbExTDnU5+Kuvc4EhxgOEw3coDNxhg9kFcyI5mHT829tky5A5DOPvqj7NEXGFqCPtSyFm/G9V+tNe4dZ54c7y9ArxG5LcSI8XIYHcBzOu7vK0jURmXAmjwFSb3FkgY8YOKLGPKCk0rJ5TKY6MOBoDWDFHM4Y9myGziVuF2qRZH7oWG8Pogu/Vybt2iE5n1V0/58/0erDPR9Gj+EGLmQPIPdlkUHP4kuYbgJQmKheJhqSxTw1G786q0x8MZEDSW7ehergRoGkyqdvuI1v9aFq1C+NpAao4puoCOK2UpCgBR8kqMYML9yXz/EkS85hKJJBKxBwaPaq97KdbSOBbs6cyFk7GKXQHtYqcVlcXlKq82+fIoG/J3KWYGJvOhF0xwAABo4Bny10Tf9uhxz7VOKEPiYC/JbNEpza1+JH/VwabH2THlyt8glKCEiDUnZmbkCSGCvSCqwhI2RZO0PfmaugYM+4d70rD/+/A4JLPznhB9TaGaHTDOjPWbWkraiZrmjJxHAsm5tC56wnChD0X1LYlIuXrxi9pWrXHghYe0OZIE1OiTq8nxp46J+E35047zDw5+BZLSzsvx07g9Mra1vZQZ0Ok2j2eifGha5Ra+juHuOw8nfx+pGhaV5i8UpMHMPZzwPXy9rvNM/c7Awax6inA64Evq5seqdmnle1KN7OY+i71/GHHWuQjbX6TTwajAConh9QBGss0Blc/W6IY289e2tAIj3caDby7O3oMVCKtGhQxbvsDI3WLHmrIEaLFXah+qmWQX5Vfg/k2TV8SqR4NBXAq5UPcvqaaxYp/exUQCLCDcmXu9AWQ+neKyKdX+d8BzgIM0aF4eFvljhCEC1K/sEEr+yPDDC9kJLhJUJK+OC0AsqbsDa2ja5PymWV4zMieRqmiWC6ZSqIk2pgiZTnc0amq9oQWrH14qmy3uDRbO1f3GDaBpxK6/5mPs91cIOR2qxMzvO14Se5VI2wqfGLZ/veJqRalQrMmH73NyPPCUEqmxi2P6E36D/3i1dtvQpcvUli0tkiAK1l1IxVluT158A53CVzM3ZsMMTOH/94Ue1yy95/vSnFPmHyDTZnKFGsRkLf1OHs2yFHNe7AxFOE7mriYjNU1f6Sfih8MNJ5mW3TdEbdG2owz2u/DL/3Mf7mpBXVcvulJCANk8Jvp4/nS8J/GvcR1XvEo6aaIv+Y0ypHx87xcd3ySSQqrw44fI0lYgm6SG5jPGIdjyL0dZ4jh2pJrPnTY8E6YqawP0aP3suUuic67xPbpfRVQWtr4qgC6Nf9HQPwPqWs0kYPCf4OlqV7mJLBwjLIOHnoN0u4Ez39/xlJqMI/xrva8nkvR/1svcUb5gCFyI6nHvlmRd/tHBJ4nYMNPk+quEKsKPpTsDEbc0FH/2/+HpnNX94+t1LYmxEe9Wy3phXsDpLaN9LpYG+uAyO0cGn8AD5k8G1GpDqK4AnEM4jly3/kqxkmvyeiOTw4xOARVpKhXebwUG6DeyV0tB2VI2PWVG6no8lOCixFr/yhDgyEAI3eDJLhWyvTwcUY3W/rH/o2+zNhw3TzqR6Gp+9f4EWP8I2adMqwbYuK6zWb0gxKjD5mSZO19ZIUkg2yBxUHHNzy9kw8KvC/EecWWp4pFWnYUjUz2VYMHb0bNMc8Yu44T6DrM5iilVed+kxTPgA/mewyfokrmn1K1GdzEUosZATkBV8PoUq1AHFUQBfbMwSWvs2V0iwqVFxt5GhGYBKfFUb/tYzHk2pNzA95j+rpRJWmXyfYw7T/6ogcb1NhdjF4FCTwMjaWtUf2eRaFs/qxOAHVaSz6tYwAImC3/g8ZKRmsN3mfUsWpRHeSsIVTCoAnyS1nnFH9Kdiu4SgBMQ4r62YUTRTCeOyIsVYwrfxBU6pXg/oRuyLvEkGNeH/yCKuwA6TacYtPGBCWzsEb0agJykQmpxDD5wdQz21San6vQwgD8opsYf5iKVEwulEOJRJQi3PA2cpW6931L9swQ02rk7dGM7idNm3wKbe0PSUN8ZZeFuV7D7oqi4tYW+TL2ZEUooDlfKZZAnh7bDiJGcrYdoMIOP5JIZ0a+Yyz5tYbzqjOqyf7wTV98AZ6Kwvrp+upRnFREGRdfx2zZXaQTVz4iGTDTby3d+QxJNnukeq+xmKxBUBElwuB010vYH6sUFY4ldP6dEWaEEjcFgRH0tYeKzH00Z+Fw1eKMt70RxpGHtul9RZ2F/zW4V14vIVELdwZBcy0AKnxmfIgt0nE14zGC2voBV3UwqUU3b+Bi46NhOUN2zYbEhCUk7tEJtj6vA+kt2A/nIUi1wuxsYGJ81Y0kGIvqX9pF5VDmlRCn8UVGQl8HNVfclb1NmJkIDgt+MSKYM6SI32lfWYk9DNWkGM9oIdNYa7iA5yogbBijLn/cp1BmtIdfzp545EVMWwfHcuJYTYD9D/tV2OB5WxfuOlbEBUSFRz+zC3qx4Gw7VmN+yp7WG7gfER0G/B5iUTo9zgTRisyPEZ4veQR0pGafTgM5l6H4ffeN+Qki6KgH0+9rvGkNM2RZdiCti6GE6XTeQaBJ7BD7PwwLXnYctEdStOsZ5rItai+RKaQeK1I2k1oLlpMNlknXbL2SazZvfmYm7BhvdCAMn/GJtp5AAAHUQGfL2pN/3AceuWzAwvfBdXv7pY955LRZqt7FuLpLpoTLuCyAyKp+XDncMBH+UuGXQElED8P6fuRZ98e1t8fFsWTV9gBrhyCdneR7Cs+WCYbHIkx9Qe4IDyUdh22ph/XHLTxpeg6gJEQTITci1G2rg0ddfvzuOGI9LCWvVC9YSPkxr+xojTu/7X6q2gcfL4I2nvocFwsInrKKjrhJuOqkMSYaF+i/GLRcvtrSY5e71+tT1Ivld9A7YZKn0ShCB2JDkNu/AcfJl81OAXMy1thSMpd+rCeoge7Xg8fRGEf9ZH+WgMuX0klmhyDIe6Hqf1OPxtxAOf3/kadrdj2j94l/kMkr9YqEqINlzsQ36M7N0/F+1Xep/V+8nQXgfOfatfe6Hz1qo3y5duv572ql1hB5CipEksb5XtqLiKKWAqMGppdC0O97GET//juGNnp7lz0yY+LS7utc8dIuVwL5Emed4EFRCZfzoqpk1XlaKB0g4fK7ma50MtaOrQ+csmM6ItyX8s9vKTFhCZQXwUec+76j/QccNz/pGKq+a2cqbd4rX1+qVq+xGgnyduuFMCSVfjCx4SZb1bd9L7Z5ykKWhdgXHAOAP/BBB0EpUE83KE6puPtR5wBexXpKuJcZF1282PCRqYOiTaS4jJram/rBsgvtYNHphLcuWpDbTdPfIi+VXcTqnoSjdv3s75nyQumlWN6AVYJoN/3H9lym0/nuLCn28T5LgtLSf+VdJ8+MvqkzM42t72o+nFvI83lRCBDIvnFT7uTBQ3qP1eoYsTiZhXfVKCr+bE4ZzwIfyNGQq0r67A8fjDvkdBa2ibogLbVIUoM+DOBPsZGg75/+++nJVSkD/8/9w484Un3/sTkpVGqeWZTk/w+MntzXQuEuAmEAZFWI0MO/yGrs5depCRx7r8skO3nu3b7EsZMeK2qrlc8zBSywB1yIItJqmvveBvKxMBUotrNKLLzIaHaJMwSxkU3yd0zEDqjSSclZEvx6WZ9A1Nhr8+r/sr2uaXAScKp+ceIojdhukHwTtaCG5vI6ek+P95rzk73R1GGm0VKIZXIem6++QmfTsMQX3o/BbURjvmXSoCpJizG67NB/VdurEkKM/EV1qxgYPTjiYIRIWWadym8/sT8XgYtlZLCVRmWTEcMikQKH6AQOVYLysE2hN4qGFLTke2PujdZgSf9SSS7S6YgQPz8LoQnl0JUiScQUMJMc4duF8HcQ51yBlr4t16NZhKE9zu0B5NrnPBVha/Y9/NG8ng94x2G1VBJiTH66ydXcIihVPPhHGBwYm65vUNajqQ9rjVNn78dHzLUOrVht0apNxhiAjZb2vrfaubG7mT+uFKSh1My+g2H6W0PUCFKTAn7pOXeUj76OA2uLyGe5M8ivo25R+yjTQOUss/YRDR4+R7vHeeYRET5SvkuD0chXmHjGijCAqV6924OXjvVpCghHIyFL/QbXSAfJDC5fvwtO4wgK1CSMps5ApuiMeNCsAyViAgS1TdKGiIxZ8HYfTiTDVqSoXJoKNGI1Dm8fv3QlZxjf1RE26GNAnJ8nqOKivui8un6+t9IVYbToYbrW/Q/mur3nbSCIC8ICnsi34WpXORuPSSNVewi4eH90wQL6J+3l3IFyWQQfJ0XmJUxVs5TzahZG5OwjzEO/r+6ypu1fUenT2/IUSdBkiBy3PM+3QFy/najkiUJwtB2EanWEUto9YcwFsea/oUX99U75IFAV3+/y9R4tmHEQRcxPJOKjfkSTKJnel6T6ytPL8/M6AlVRxr5F2nxe8DeQkTBkaDRbD14Ywo9BxjMIyitbLN03z3wYa1LPdSoBWtNckrOZ/AYDHDMZyZBz+o33JwjUfuuxBPgz2nSLrACCd7wqhARR57jKlyDfWQYFbIZaZ5Z5ofa9VZNQNAMRd4dk56YVUHNl1pf1CbMVbjwH/RcnO6i+XdRbvQGrRxq1Rw1fqIL/vj08jdXqxpeN/Ieov+QDI3X9oLls38GANzA1F0VlOmO1+X7L5AHbAbmjR8l3lgBlwWwwvjJ8Wick5A3N32RpGLySdlRPk6GsAGbN0i4S8PSHWErsk1bp9Dyh937aEC6asQe5OzQFGVnfJIMqqmFGTcZat18YNLjwMD50th2vuQAPBhcf2avuqGz2bz5yCibOKNxfgm7sXDYWMfNEbkucmLDSKgHqqpqmjNTz/X+L72Q/5x8FHSOQYIwbG2kK/gHNnYcmfmlpNPwKJiriECk7YD0PbkPHCKbCOm2v14R0bZ9mqtYKCfV+MT/SicUp9S64aIoamDE2g4+dcuLGn1h2TRHHbl79490Hpo2zl198bcVQoUCvf8AWzYGwRbsnBr2PT/Jx2VI0J8gn1WeeX5IYu8vNNs09vbNWhKHyJjAfaKS8DyLSwUfcY3Jw/TyL68zd4eAB6pD8vv8QUYqK9QlF+Q2hu+s98Zlm1mnSyAAOG1/96vUVG+fECRo2JDB6Qb+nsWJ6ZQZs8Gks5Hu3/pD9431xFYAABNEQZs0SahBbJlMCX8EmprSD/6YkFlVon9pkaH5xRQnPxQNe3xvogy8wNDfeR73fkS7pxwciKFgU8ELilVWDhXUKu0wh5ka5GjtGdUeUyqE/68uvkcdi9AYXSnoA1lwAYVAxbhybFo9cOHhXw8U0u95kMX+TOyyhTitoHrYpsdzgjnjU04S6YZ/Bx7t4ejRWm6yFLF8GSAJ3XN7ZMRGCHnGxAeBwFaRa9uyIWALViTwWPPS34S86PqA7JbBemxAlbpbsbmB76r8i1F17zOmHPxs0MJuMD+XBLjF4N/6X3A/FcWc6/HXFlmeP+/8wso+qcseYjOI9l3HHuSWC4N7x12Ps+Xh06rQ7aGFS3mVdk5RcMDkPz9mZyc8kugAz7h3QB+o5zQTHo4lwcr729Pdiqyoq80ltwbU9qSSAvKtoJBk48vg60Cte/tQikMSucxbO7GZ60XlEVFr00iaeHEWk7ghN1N5zRz0pQUgMRmEzB4WJqPmPABTd7ooqjtYu33BQqXywX1VfqidYbZUWFNF0LN3Nnov53f6cHIjuG1yPppJyK5rBrR8spSwYlJVj/BS7apLufr8ucdEtaJsQ6r5bZ1OPFA4641k0VYCNJbkGG+uC9IcvP3qY7rct0HcTAIoOuQ4pNn+iG9rixGKz5WyKg77dqZ56MWgbf7qidQDIebE++6SC9DA8wF2n53fD+KvBKVkEn5K1oZHsoKCJsVfvW4LgUPFiSXpI5O0zDo/jm34+HIcq6e4YLiHKtjpFFy4iwAV9igMW87E27sp7gCuiHxwCqWlzahsGCdKzifsBIUi4RlUklUNRWd/+XiMjD7yrmtEIZlRQv29mE8E9cOE9dkKHaie21q8fVyxCn5fs0vQKcvS3GYZgb5LLpHj+2YUhGWu2p9O5yOHNzBAPknf5MPgj50Rv2xxwE5Ic5DmE7PYnKHJkBSF7s/8zRldwfrKoo7kn9n6ft3Zv1Uk6iM1WgHMg6FRyQ2iehFt3hMsjyRCc5UP6jb1X2rENAGJQfCYlGOqh/HZ+fQ0EaBppuWFZdcG7iqNDw4tLEHbSmiQx8C0poOMZO43AJqE6qKOtw44vtDKVPFbHt4s5epqGmc1rg++wfh9qCg/gn44XmnK8THazMMFZ+Din7eI/BOhRUORt1pm0nGU0zGbQqxwHmQxjUMYZN4MppZF7k7ovUz3LEb/A8rOgFi31GjZ4sKbuDNr4nyE8XOxQ/4MDivQ1HOx7m02ueQyPAjl7g1A7mQEGij0hpDGQcJaWvzC18i7lHLeD7Nwcm5cIxWSTqpcRU6sm6+MltVVNSOvnin83nBJ/2OEtmNlHYFc7j7eU2k3q7roSR3E+Ke7xXDcQib8aAkYiRoJBsRnzNmk2i+GNVj1SrVBtTqtQ4tqoHvXpR5t3EDbIezejeUoKmH+HykuNoTdNULwAB6q+MfaFD5sbA/k7I09xf8RJRX5lKuMYCLMpU0PLycb0H4RG5acaiEnOSCX8f5XvMJGUAWMFVIQ0sWczuI/3bT9vIryUWR4Uoph1bBjHpCg2JFk9Ifiw6HNSEGvwpyBZdr12VH1o7kBfrnRTSJcrhzZeDdBWL8nAVTkaZtYP6cdJ9lze46H7QktoXe6aDw92dUdUKwxe7sZOaC41rB/0Rc2rSIUFa5DbZMgLOvewYmrv0qFZpwWX165oTCNmhMrUlpSxyltm6OugG3i56WIs07pfxDWaeAbTp2QpKDxJHOQu0pJm6iGH1/r36Q5Do78FFUsXe3opGVZHpkuYQ+vNGYz6ztNndL0oIOSz8xkVRr+R2nC8Xz8IyCQNncEEtGATAAdtw9/vyiW7i7LE+oiuUKG/tmxYC9p9H/9HZEpA+v3p/8SCw3/jDkgG3N/oR5GCTgqdp+fHFSVd2NJbA3qZP2Bv1yJaIaIDHwtQjDV1kqIe2Q9dxS4LHTyvfFgcDNcTI1EGhVPQU/eb4gvWU5ywpjUC5zrhwLl/TchG1Xr6757Camheocc/5WD6uqosYa6Z3q2lf1TwJ4SX2kTlA1mmW0oemQOYcOsNKHkI+AYb9gHuv01A5BFqHQsfqnp0qMR4oGPNKYmXFpspnLraaMXSKBriCmryhosOgMrGV9MdjmP/zGIkgGL39kWVAfNZD7xNT7oKpuUft28G2dRfKRA35F/N1nqZsHA2IhNeZTHSoXpFUHswYCuqyPmIGhp82tquyL/pCfShOz5JCcrnucvuJrt1+SuaOyCpx52w2CLFBLhpYldCKlhscXnkhVtg4Sd5afFqcibhbMgTah0TsmU59YxQaqffav3uAKUYN5s0fnlpdr5cXzGugR3w/0BnArCpd2vcFH+q+t6zbcjIs9u8PJfobFeuBMUibcgm98ZMKVGvqZbUSUXtsCFjgyu1MvH44Uh0PPgU3KHSTJ+hAGZigt5rmEh2Z5SvZQjST1aa8NDYvnO9fG+depYAOY070RR+d4IHCJ9irIDmKmnD/YsVGOr4ctEGJyMUF9x2hRGmkn0tcIF3d+rm6f1MZdND7kEK2v+aL7beCjL+6zE8tb6RbR4s9ypl5IfLnsQSAT/fBkvtRPoEo1u4j5HKJGeFxSsVKJfHu0zNYy95SsAmFP/OwiKXlbCQGnT98ioY4V8rWF+whr153mvs4FMgDK4kv9TFhAK2w+KfWZR1ts7OpPhY0z1MfW1XK3DPCxv/Dq77whShGg+ESrPhmxlUqcj3X7vr5/MegYDYs/Lmm6hA7QbkC/Pt508vY3ABgTAhnv90BENv9O8dwKsmTjPlXdbLMunx3E+AXK4RlWbz2GkRhxFAHo3AZfk1SXURmUUNdXbA70WbRBwExu9AbfmzVmjrJhjHKKzzXSOl9fuT4VtBrJo85Vsgz1p4EbwsHkrA1KKofDA4YW6rm3WD8NI7Z3pQz55lgDXppIK6+ngqTk0X7TtAhLKAT+PIv61VVnQtBJDbHPst9NUD4wHPjDg5SxdNTNjyxvMz6JYqUZFMagxzz8GlgKffOvZlECJFjyRvXHPxZI2PVY6IzWqiIFRrSWepGC/jMRWwkEbqri9Dh4MyjicI/r5w+/zTSyQiJfKQUWk+lXg5fkWMRppQAgl4OtGVCnOKr5U1kN6iKT36Y0gE265InKcLJMAk3CA8fT6yEl44Sw2N4oYAtQnbBR9AfE+HsMPzBE/+tS3MA3IF8OibxuZ0sDMOrhpDeS/iua1VIyhs6Dq6hr3LROc7LHRAOYM6njfWrT/w8kyrzuA1u8bQVDoHqmDpey1AFu7CUYkbnXbxK3yrWYI47h7Zi/c8mxEahzYp49x0YjQdz6keB9wKys9pns0V2lSD6eezvhPDPsKBYj+VulrzgaoZ6gYvcodTg/c0tINLINLk+kc38knunb+bbXx8/ztnYT4aJK9XsS0s///17DioMd4P/niQ1l9WdEeDJKuTHFYHfw51SLdlaHIb3ttIcFqHMnClM0awXi9XGxQHg3EUG7JnFSxzAJsm2NPZyYwm8s/EcI4FnzSKSjOhBBgjae7qlUmdQRHEYyEy8+lFMsr/PYfZeIihvWbjB6TlcuDzHgIpeu61jJMikn+wT5/03a1nEyBz2nHXsZaAtm8dK2DxyQPsYzlxOzp3f2/aO38x3uNhNUAA+Zqd09QY+wtQM1t80CQxQBTutFj6FZz766EaDvnYav8sv8qgE0vEMDCgemKwdy0Y/bPXjoRVDiQyq4LAlTq9XmTXnQNTKnq1ezA3Dc1UdzurmBAbKOIosp6VFq0nccMgzRzEh6p2addeP0jZF8OIKe3nwkO2CBtMUww+v+X+k6bnn/I/MFfnOdyI8DHJHiiuE0Je6Z7e2j0YjI+vjiB5LayqrsoQddaqZcbPuXSWGSNAXcwHDQQ4EMgDyseDnDqYAUn/58/KmtASWVmftrBN4+Hxp6uBYhj2uRX0HfM0Ga0RUV505BuOLgHMKvbN/N7xxSsi9xUXWRpj76vRXlPX0cKw1SX2LRX6InHHMOvDKQuLkUVLf0ExHWmlRw3WI0qyghxBpsYkrA9SAiCStEbzvBv5S9N8B/KJ7607+4pd62G/O9y+fTwa9JVycaLFwyJj8swVxLn4OEgXvMPfnSfdx2FOCBTGWTU7SpqYGEDMKof1iS6Ts5DzGUo6QsiSMLFOfnoKO6IK94J9ASU15175sjsAsSEsIHpk7FmTEVia+gicphujMSOrfaLgMSJuvzZCOFsvpwy4qrpEjkLmx0pUm9iYbe6ItdmAF80NGU6mNZJ8hut7N5k+DINPWhZ7wbvrS6JZkT02xN0v2MA6Wd2DTGINx/MgraiQ0PZhYsMs6diMuu8EjAAUaFZZDI+jcOrD+pNon0xpwy2CNip+TFDLGPYrPkYFqceC19KX6/nF31ifBwljiFr/vuuM1PdFSq/jQtHcwQw++bjjNTH+ms8qCk8sS3tskiM3gWzYrt89Pp8D29cFH/EhpUT/UxPlmy498JEVTLt4Vw2C+B/qrCliMRrBeZWGo7zHSLBFRonoXYtsG/luCP7n/r5/XGwNE+pSuzcm1tpzLH/0Pe9FlStpikoTKH3LJeGMVxQWOxeUBUrXDYzg00pTx9cDVaw76d3/dr1NMmhYbJ1hM/qVjThMAsIzkQ66FeqEuGLCqcqUJvSuph5twap8kjjoYDYVBrP+KVKVjuyvIYiY6pjPwPh+einPa0EWI/qVjhqeWNUTJE07kpUNLnatiBwElCaAc5S7JgMJ9z0A46qxV/t8qdHul8MW8n+0HCzo9+AV7lt7Hf8N74TspwcTf5QXDfsnlAIvVMqrXqFZJNlrMJ1f5oN8fZcmoavGHVDNUR6tYX1lmxSpAA/JVqbQrtlBPfJJ285FTWDcZpLa0nUMXNWfrooxFOZH+fCfk/NH+RGA4+d1Ux2QqzYkjwQd04nDOmP3FGQNmsV7cS6fh5DGmTA7pO+0/lwjs5lA13t5aXfQz7/IH9+OVzW6CjrGdPs/5mMMRndOtLprvLgV/oQtaUxVmREW2tlRY5rUPZ5NHrrWfhgVAlb/ZHhR+CBfqx5D8Fxe71KieVylLFQEYc9vpcujHSZ/LKgeXKv/9TEBjl1x9YZyGLICXSU7Ctz4bzE0L02xsaqebXv+P7a3ef58OUSCl7MyhuUxKJ5IOliPjL4IQdQrjaa8WikjGAiLwvyUFucWMnOn5mELMDtRFEc95lUa+fwTP3g64ZO9TmNhDZMqBuoDjfXHnqqpcBpWMZa6wXYLYDYGt7UAKzvO0ivlIDDTh9PtSCz/wa5Ulj4XsIahuasKzOf3bSHF1Yce5r7c8c0j4Cga25ExvJx+LegXm+efAf0rV97uhU2vlY6iKJWvCe83u0MT8OL5L1R6vK/nhDVeVjXJ1Smt2eDeZbzdk8m434V4XQUmwU2J0rc6aJ2NVritjFZEDTRu0UO9mN6Hp+uW0dfXUl1kH5pIREuz1N6jAnGi9MnEfoZKZ6oC4cm/ZBvjPBzgv3zQti5bb4L/bue/4/lM37uKg9nKT56UnuCf2ekYdO9Su6pwzOEULcXCYCKqwlpKSsvSLzlUxNQD+gHgDpKqcNqukjKX+RRNLBWOsu2iqIvFMRHUZYcaiteOYxNFVMQ4yEfu+9+lq72kklX1wRsQHX4OID9zUkBz1Ur4NKI1amPcdWJVJSeGWF4SVKseEhuha2hfWWyM2PdN/hdY3mmc0MREg2FeY8P2ZGqE6apQGLv2iidFscF4o96ZvEBSwVN4Z4Y2wvdc/zm/TcH0KpPRtCw6LnCn6QSTe1Ie6QpD+XQmIA3+7+eLwVWmP9jmgp60If2O8t+HFrr6CseDUNsB4wQLUsmatnaI7+yeg4ddEK608i4Q28PqXmc2akUbRFYrNZNQVt1ajXrFZN13hjOyz2zHbBmHCcMRHvQ7Vs2FUzCEhTJbIoUEXeVaIxE9HOrFAuPUH/ODayS10CbnUVv3s9ojPDsSoTV9MUwf/4p5eRbKcCJdPrgdYMQWTEFNjBSQmqrEytA9FwR1OM71IuBaH0dSqB3MCelcIhfgnYu8Q1svflLReABQEg0QkUknwfT5Zsx6Ls4v0UzyQLVmkW2hibJTKGvsK2muInQ6M5AtVvq2qQq7p3/cb855Ejo35OJK1m+YAAEzaNjLIuZIA0z43V9EliOg8whyl9efO4FT+POKvxa95oOVR5Mbjb2Ad/hYr4j5fmmUiGf1nrdAAgN0QUspnFlVXgX8c8bePgJVRod5o4aDLeGRV5ZLGoNFQhTQGZeDxSUEzwKPp54UlGCRjTXw/95EFMvsZW7sEwlcU6H1Lz5+wJv74m9bZZZbRcBZ42lvo8AHQ8aOmPGHY857sv0J2MO1qI7mvHrlXsstBgC+g108meiXhpLzrik1Ji7oi0N6bDc7HRMGKGKW7Z/ZgpxUt1MW/TkS1lherUJiAP/wIah/BN/y7A8EoLoa7rZ06KiLHUgc0MW6Kx/KxcqriXwHGRoizFCcmz7EV038CW1jtMxNG0S9MBESEasVtf5khlxhzrJ84CBmeHiSrY5m/lfQ4NkGYml+qU9a8wsUTYlbC0/2lSSDnf62cr6N7ZWFMrZQIiACr/RZzAEQFee3A+ayQ27kk0YYwpYAAAKR0GfUkUVLX9rcjKdO+3+gm4NXXdEFiRYABTANS98tNGKV4GNEQkIDWmrVAE2YmCvf9i9bAJIBgx07Z2tTvaB3V9slhUdhP6QhBeiUnq89MRWhwo8BVg++Iqa4tkr5aW3NIzDBS5QsMmQOA5hpP/RbtimhAYt/F/htQTC7EXFgRLeImqF06gZdNRf4CFW0p84MM7+LNpaKU1Q1SWkEKrN6ZDWcVMZFgR4cx1kOp2S9FwJJltph+EOqKP5SRnHMQI1R0Ise/yTajNSpauJYJGPkMitxVjJa8v5dx74E6SUxJQ4FPCJqKwKxe5jSxB3SpYSKFiAduVlYL5TZYc0asKKonplbN10b2NgcaZvA8qrm+zS0czfjxN5wwW3OvGa9jPDTkPoxfyhlYx2sXkBOL6+caHVGiu71JAxMcjfn4I1Z0B24QqRljPM5H5iBjSv+YvfjwpHLHre3ofZcHXsU7L18wi2jcaBFhhUczIVNUme41lhEj/ttfT9J3MIq0XcTZHH1qTl7B8ZB8kzUQo0ku9Yw3wnuzoO6ToGVEKQIhRu8nf8xWNkekVAGj4V9W7v2C4W0lsjl4pYqL7f10UR6bCrowIDjoYmT08oB0X0ax0cnpDurUFXTQk2G5rkBDq0dZPNiqT76X3qY1ktGpkLL13ek82rIuJ5nxonehrMM/uJ93lpfKtShBeWtvucFqTnx+TliWbopv90sWOtsvt/btf9xF2NYoBvbPHUSzXuxOybKS6hnu8uAGsFQvmNr/yo9Ls3vmRkOclQV/NL7tQn4BrzX8+SEiuV3SQLED3FX/PyFVt5SJn88+aeiY20H7tLtV6fRBvVYcro5MkIoknxguept2t7aide9auSwCejypN6BpGDMbTEfSLxoAcrvFHPF3axZvmYKSJ0hS1POwaS9Kwm3l5FHkL0vghRNvsbcf5L81cxtAATxwy1isUM5jgZnntcVf6qhg7oTfvZGMmxk3DgjHStKOx/V5u/MeKrmLf6KEPCqK0xP1wZoBJ/qgpopYeiCF+nxasWEZKonoKZfr5XOlB/v/rLtX5lidEMNzjQmBkTx59oBhG1MXmP6rpkZ5SN6qSX8Dw+WEBW1nY6Csg7HYXtUuviNvkq9ZzEczn7D7kO0Z5ssY8Xafqs0ufPqYigxW4UGQd7cMXLIkJPdHVuRrz+59iLGl9f/W1QYld5R29Urn46J+JndThUMlXHIWYjcZ5OArFEww0y7C14YJo1gVbOI3lyArZO0pagOCq26DBX6jiveheSTuDS+FwD/dQJfAm3Cn7PYtuVY2xtrmQkWPvOs92JlDp1XlnYZ6EFS1P1L7lcA9gs2YGU0SfILB56cCNzlij7WGR8AGBnZFZ4hSnVfL97e+yCWW1PILr+KfROXquUC9MP31flGwhl+WMVThaVO9tw8lUA1Zivw0JTH7nCb6fIPandHgH158AKjsgyv03gDZKfnR97b/FZiMV6Dene2X2R1WRlX8iJkMjJ0gr3CKJwZduocnt+6Azl9jsjZrysgKdJaRB0NHkvFajxIqRkELDI+yq7IGgjFILSVMN4Y6/QAHDKyj13PvkDdO1+mkKOGOO/9xhEWA03bPEl2GvkUQ6dpETjDQPly5SvdXjEkIPZtYpHNxC167O7ENj6lhLAeuOkx6AKUrSHphul+E4DUmhcqCVwdcYyWkglqniwCZw5QXYvgrWurEm7SPBjLIcw9u7klGFq8zkTy14XyKuyL5rKH3nLFBIqQFqn8Ma5+jIvleT+BwCEVl9mu9E5Fm8b7JRGdidKVoqe3Q3yQqm2LnXvXUa2EfEiWQjunVbuRoIO4DklBN7pt5GQt+cHr3cHH199StPASc4OJHC09rzBcvw16kN2KA7munBX3Gv6Jo42GdY0mR/M7KM+H+HZIgvOYSijLyP7X5eW5wRV9KYBNAI2lOo9KkEmihShRjGlD8ixEJ6LER5waFCjB+4wr1AiUfSsSZSpzmhY3z2cdpjmicl2dx0nS5g1FAVGPCh2PcfDf0/204lSeJ3OcdSW9Bs8MBX69wTm7GpleLvSwnRrBTd7D2y9KmVJQ1AZtyi38/7O6hG4XoeZ5mISBBCJwtDj2hEoNYrkpEMYWdOfOP4NZfqTBZSLrlMDd+vAcb3KiMLgSZrdXjswrUxPrlG68PHUQXWOhRjAX5pZbx0pZES1n5IThcZM5502R5MSTOSto3Zkv8l55cTFAIXGv1SFpGgIsQMZVuiaoM27JEPGLcwLP0tKRAJGpcGA4TsALZWPwpRUVFYjO2oQ6oDN2futh5hDGb8a2xZacJV6CRkf5RbsLpH2I4Yg1Umc0GBSIZLOE2LNVQh8D2pMErmAHGf3i8/p4oxs1+QZ0MLXa1cbhA2EF8423aq/mZWDe2zuLwYhIbmIBZ7iOKlluIKL4lLR3SGa6KrhbjToXrq6qxnGZFGTgiHn2eS6nqZJrrgLj8Ttw0sgA/7DsNYpQ+bs5EbQ3lUvUTwmUvDeBLPGdW9ZOCQyZiGGy1bTNUA2lbLDZpaXDzp5KGN4NyFlBOqY57ChYi8MbWiJ7Cmh73f1C5XsD9OkeIZw9lMJmczO+iCgVNrdVAgmXc9TzOfnbtwtLMEPe6iopXcmh6j+JaMMdADOViqKLCopJoph6HwRLOe33U2SV0z6UqfKxBm1vFSqBa1vX6T6jS6+vnVH+K/lBQV5GrbvlM1EJqpo3qRx5qNK2bwL7si65RUKdGOZ09FW4hNTVbMHmF4fFIWdLzq34YG420m70RTcNE7wFC5GSAd1/58rAQIS2HG8Wo+b2cqePnpKrXBfxa3I9EUxvg/sD61lonAwna+huCrmJxfmEJvXtSPnIabILzvYyV3Ss84R79LLYwu80Y50/AtU46hd3SNi2UtLfa5netKCtbUAEgHu8mKsiKxEVNX+Pt+HPsUhXdTn/qDNZHR/XwQOKHyt59M0E9uI8ZE9r8yoe4InZy0hBgCNWzfzWojoL6dTkEKHOYcAv/JbZOVs9Mj2LMrSsVfa+a/yI36OIc1qQI6OXDh/6Np/z5v3le/16Ik2sIMeh9aF6Dlee86NuvplkzuTZmBdZCtJ2q0AxFn4lJG3y1hok5+VYQQTpLyN07YzE6L8Zjo39tYIEuDmQqvqz4K0Hdbyzq7Qz2LlaW9dFbv15/04+kfOY2CS6/JdsOf1nFDyLl4g1d+2bJWdA7ElyLLkMsYd/1GiD3nb4LNiiva2ViAsvz2gcLVdAgAg96RYRsYO0STilHbas6w0d6D7FiUZ8egZwiZzdQ+kKzMqvsI5AT2fAuds6BJ7w6FK6KLd2TFiKVJrSPuRNAMeigetSJfG4ZiBDfGRW9aiMZg8ek4lHM74Gd8J1EFkCSR/2oHQ6o50pu6Tlz2Ri51ypEzuqdcWBvWLyScpB5iOw60nO4OluIgQD5b5Sm8hGhEYvMo8/za0CXjDRFm+nCIacDEvs4BXrhbWQ0UkK4DQoIG+4fY/Ggo4KonGgc6Ejz8KwpUxTpg5RSK09bCHfPnt6QAABhYBn3F0Tf9xZuMF58QexuyeNEQ9Df6FLKtOU/hPHk0oj1fwJPR8Hj8o+HvwQz1NJPtXPt7k5EF3Y8RzaoiyJy9P3yKokguTCYguNfrXe3iwDptBgZnrLO6h7xpZjgX2I5vqwz2F8sj5/Irdqi+3Gj1fxaGufv2VYw59nPoXBFFQFGRQ2EBePIldCgKSXUc7gAEJif5XHn55f7B1sZvDZ+EcqsYkWU6a89sZbxS/AFfHTZryZptCLo5392Ibx2FBm9wAPdf5zcCVwIm3z2tvLYqsBkdSK2Z7op27AVLncToZYH0tepEmkMQE4y7fBGxypc1eoOBOua4QuU5rn9yucxVvoOv4gHzxovqAF8HyAs69jidzlLVtpQ+dOETKxbeYH5tthvL/jZ7o9VL7zLTS0F39sHuDyGWiAXgS7K7hhpDwRgwV9Mu7q/CBTLhPmPDgOxX9mh78BPcONGdfmPj+/x+hFQfUxo8npvjJ0o1El+9OhRbqBKFh9NQpnsyrVCYrVYKG+jArlPCmHyOyr8iX5ALN7zCDAEzs6aYbqbS0MI0M3AP018DI+f5Oue2LBfsdnwNRgZkOe81PbbPFHwkjqCLKcZs67LHjcUtUFjadR+vula3aAoTAoDnoXPIiSfJASXx2CFFEvVKcjd3J0EFhvamih7HpEJOlYD5Fmui7N+vTm5+tdlLZcd+eYt5Euceg2Jk/IeAMoobj4CYn4poe5P9ljy1KPbTZjwdmv+B6V+C7YhC6424gXIryxMGfQ3owzuY1EB1Y24AlnBGaVy2MxB0U2mgyl1VZdZ774Zd3xopz9dPjlhgzNCPV8tgcFFgz13FUTTClrfOsbl/5XNbNoC3/nNkn2BJA8p3dmCCMmLPsynD6Fvs5GmqPdWBT1LjSCXOux/bkacPgJqoETwHhdR+MEekmIOnzeIndQ8zbAMqgJg51D+QTEXy94X24At0SWR5J2dZyhJuLhzfgPvpJuZedpiCNdl1l3IwLftGm5f0ecFhj9H2NgUEJ3aHjdPFx/a63UgInUkKRlQ4hZGL1NZQQH7qUZRnAM2iJbowfeh/ThKraCshz7vGMDkMmntb2D56NAHWCbwDLAKq0z0nQShS1fG96mFwqbdqxWsZYxvlCy0rTZDB4+9ZUStBZemXGs5kPyFGHVDkIvckkGbN5lXfeYqucvrFbfbyzj6PLNi9Kj+j5iWXkTxIjH38mHRfnNLK+OfwHQg0ZEv9DAca1El9suhpAm+ZHcMqTBtrc5rMp7tqNKFXBNLHr1aN8uGKwxBQJgtiiOxKCoWY20ds6xDaMTF8TJokLmv/HG2v/xeQmSm9twmRrGf18/+sN7tco4i4vun7gcOdqPRFtBbPSc19YloqK1GwGNiaKbN8vnELRtHl/sOZSA3Ixay9l177qY2DFSkyM7IhmAhhpaLE2QiPTxTNyI9KojjGflm1apPG7KGPWkP6QeRy5MYb68fMuv5tM4RiEO8/PcZ9f+cev7aNgQ9sjGEsNZevLxWNLOUpSb+hBho+mxyVDPpDiRiI2R72IchUzN152I7hkOLqJ15J9xwxMbZooIIbCRX1BGhSPkWrr/qbTbNM2xUnnJaMmq45LEnSILbVFFgON+X1kINUvaMB/wBJaYF5V2rSkPYp8K6VtCgREKW60+VHX5YoRdP3Ux1GDivhJtUj3FB0R+sq7YLKAHPpG8KfvLJw40atqSFhIRmmTkaY9GA0yf0rqIeqhkO4DhU3AUKEkg3/tAQ8sAZ6nd5lSnMkvuS2czGE3ykYnIsiiTcXNolFvftzuSAVOqDd3SsLV+NtZmfH+ERu31mBxzt0T8DUNYycmCrfS09IoRR8/luZGKmvPBCIfvYd8FBfS8NyJ/MS2paAmPPOGYN/fzmPuBCyXSR7hXrvyBYnVy81lUioe086Kej+bpN5c/6O0qg2PglCKB+btIB8IQHnSp2ttbJG5geecLzgVLVpKj8/BDsy9ihpUAXSh2mVtZ/mTPRYW5V5uFBvo1Fo4lGI0JJ69HGTVFtS8Ku5TJBP32kZKW0fLCD/NlFIz9W9lYCc6UGFHgZkk4+CH3AtODX+mv95TAAAGzQGfc2pN/3YDQ5rLeglr4WtQAJak+mEf2o6xtkU6jFdxSy3bam01j/RsxQurehtsjV9YgpADGY2+AjlP2w/N2dXO5NOT/+F8ROZCtJT4tByvihkZjhKTeqpLMJy3A1kG88AK+BY6XYT57IOPFP8KD+IjEkQT45XjZFhF++zPbxa0gsjqe18BSKIMAiEiqfjfqriNlsv4lMG13rlj3y7Kk3HgES3HXeFm6vn6bsT62yq5RZL14IvrZFjprFWAtVxkKFZWlHi5J9nuINHH/02M+TldLbQ8yZbHCTBwkZoqmO/ITaGSmwm/YMHWcCQZ+DDZu2Glg2a0UP4RQ6XLnhDfJgD/xyTBVfCXVK8cQfQ6X4xyzVodIU4Fx1g8Jan2gM3yzs1RrzDc0s8yqsl9hr++cLq+giRSH4MwIMdTQ/ltGgPw5UQmT7go+NR4ftPO5XdecMH82Ga9xi6ZoTEB/clBFwymNo21WkVxSn8TsuBZILQZpe64xh8gSK2WLlObpfGB5e6b+yXgnmfDbH4hxlzCWghBnCDTcG21U/O/Oisr45NXfK1YYYeP+JNxU2YBjXv7uL1lFijHwVPf3huIW4egJ9ARYCtNN03FM8x6COyezoMMkfOIWm55Z8GGlxdNr1u0l62A0nWuGQLGtpWHxfwZj/1Rwwgrgz8oRgnxJz9BTL9UWFr2Z4Izs1KiiLYBchXn0FQ7OVkGv68zNUCWpRRPq1/tVfP2JRARajgdRC1AfWExxGZ2I8CBY62g2/MkthKL78GGhL/wXFqOGnYi2aJ0aI/BT6EuP6AujCzAXyVPiraguJy1Z8lPsGZKNxW3Gm/csiRxFEUut68vtdr4cZzUC0L9hd8C1BFaEtJ+oDqwlPWq4sid0Mrq4Vkh37zQAWFgQeiVDca74Bw7KBaPDVLCNztZ2grKHaKkqgU8W7DJfLU8kbBy4giuTA6WVAFvhNWcLK0nASkVikyxKqEOrkXj4AlDMFePbKMAb/9Fkw6aNOU8v9PaLpG93jLvCVLyDQxe7CW/di+txO0ASAP6b7sf5bqo/zE8yq3sOI0aU/T22IiC7W0vGbetvm1rBjNx1p6rRN8lF2/UMOEi39vuZoOClWGuR6qu91D9qayiwQDpdlgYroDA8Ag4zFfiQ9VGESPwgTM425BmGdEkAEa8hf9GCOCd6CBq351z0uuoAMLhGgdCXuNShD6749kpr2mEvzORchhshKbWVDfd+r/rWMLrFYs9k/VlsWVU/a+zzFz0HfKxtNZc02kHmJh8/TGQJ8UgdjG7ApyTUKbN73qKk4qli4icr4YtTWXFEGcDhIrDirx/aTJiFAFaGsjntNIFZlkugx2fzyUm4K6s3ws7O7FlVf15s5oiNKim9i4LKHyumc63vvq7pfkQH539WZzmOER/G3KLQroP/26AVuiZmvpcvOAzJPmsXV8QbK2vAP6jvh3uEcvAQca0/p3ipwt7sMsNz+erPst+LErM2qPAj1B7s/+bXjQ+GeP87cAYvNP7jqID2z3oLv0dARASe1dXNvZhEIMt+/qDRcJXn9ihrmgq1UHMDVPc7qHltw5EfvLResq94kHOvTWEdVw7ccNQcC5gnhwFM4M+U3LIs/qEA9BdOaQ2QBnFz6THllO/sm8vSgtZASDISOa66DseNta6KOTo+ysjP00Jpe2woH1HgNbNkZUARECklt2VKpROOqrrnl1Odt0gCu/zLqSURRjgboauh5HFSj4vUzubtoGrkvIsCtUdoaFGY/GvQ0VneDIfDxJ173igta+egZOwaatRn4d1ouhble3CmytHeE8py4jJJU/Urp4WI9RwKiHTzvl2WeB/kyZv4/mjEdLZEL4fDpDInJhYxX8flYoeccye7iF40uH67J7bohLUspFfUdOeSTEC/5xn7N/aHAiiBscm9zMxP5dCHr0J/NBxKLV1zz4EDR+/k+brH5fJ1s9XfQmDLwlWD8CciRQvZocgG/y8QwZKvTQ9rQq0HrVdIW5Pdes7qVoOCQe8VmMZeW6OoQ7+YEgKtZ6uPE4Nm68dRK/ADt8oqnkneE7EB0SXPZlp6kSu7c5CrNLu6+3ObqbEfcntLibsd6CXdsx5//lzBfwo43NGhQulSOGWOZMxY5T54ba5MAtfuIIEDjWKsPA5X1TcqbG9j/diVMyISRkRLrAPnDjNuJnt1GgmPJff+WPfhzJSxYyQtl32gNwe5Lo5wHfnIdpMBVJlS7pv3CQPwt4VPVzCBFWZD2iSkvylDJrtzNC6rje+KKV4gSYp6MqDDoXbU6grBm9X42MR1y/DQlGTCe7WfV+PXr7ABkHHSZaSwMAAABMmQZt3SahBbJlMD/8GxZD/AaqojgWWcSlubn1WGIz7XdFh5sYcKHf6A8G0VNXxDhOTw/WCCwFBcboeHQWsPW/jC/FjwBqRsbMx2gUerVqXGJQtyrNj8s4CePCLBPKZxAiFk+X+saA5GK0LFWBemdEEuKwnAnhyp7rki+Tt6ExYjZsjn0UtE5oNee434PkLuSG42zI7QHMoQLW1Hmiv1Sgi25AehgMPkwN4uWC3W2G8MI+0dovBTJvYPUc1Q5AepiZfEBr9CdC7xWRaZZ8EeZ0/1/cSrnveOoMpqMznUKavBD3YY91JYd9yRO25ZZAqkAb9hUmyrNDDhjJldzHApTxsKzQX/sNJgACDpcv0CFne9tJXdlAQoOCjVS0/x8dY9j+ImJ1YKMBrjxUdlSvVG7bzLBXmWYO8cwkXq5GRH1t5FPQ4SA0Q6nPIoS6Nr0GvtpWMKCzKi3PKwYGznT3Uhe+AMeGaOKCtQHkjH8//09AF8BDf/RXReZJFrRZGvLCzsFPNBOxXmcB/Ww0yx6QqyldzCBQfvAcZPqOW9J+ScMqKj2seq0kA/13eiJDcHmQmv2Mdu8aBzpKKMpVz10g4m1LlHxp3uWYYzoLoGSeg2BRsYDHXkcbNc9GpEW9iknbIfKCXv43c8VjRyyOJlcqc9l3PoQ2a6/52C8MOd3DVmCw3caps8Q4snCv91fR4pD4TaQYEJpZrK1lHbYkIu/2BTPSXlWYz2FUltI4NupiexC6FMctV1/RCMsPSWGi5YPO2PoVGC84tQQVbqQg4J6LBQTtURhikDOTnx4mmGT+Jsxb+p40EeGb9cdnBymXUCbpuZGPhYZpImlq5dpxJwsqGuTALU+ObxF16p6AMQKKy8Izp1kt2acC7S8a+6rYzfyLPid9tYeF//86eKa2mKIC9VI3T1DZgR3yRXX+9/W80P66LesQ6ybb2mosatdOTdo7LC9XB3Gu5INsKYFhkCcKyr0E0Gc8lIIwkB2ADYI+iJ3BuqX28Sfi9lFRHsgNb4qHYB8HVX13454c+xCy+enwfZIqMBa2QgaJTWSXXEuNJ1RvXD9YT27j+557F+EFcQ770Bzzyf4W/oZbhc1FEruD3A2PiCxjbB4oi3R5MUGwXMANGvvUHZFThHKGcKWfDEhwAXGQyaPC1kHAl3Y5qEiROGw2JlrUuFjxhvpoKGKwHNzOiUAjN5Sr9kGotiihfCvflWA05pmNWt0lEBPJxdVyV+4cJzeY9NHqIajxt5EfpdmchIxGmMUPz4wZB42YrW7HIRJUC4bK1M/5fJe8nVK6P8jy1IEO3VvaxFe80efe7QTJ/SUMx+m5vtFDnGJ4eZeUO4LvhlNLcKzxCK9Nf6TGePQszl69V58klYitx74nx/TvcHgcYKEl6EY33LhvtiDCu+MUNPLVQFw/vTcZzvUMRgLEycD5AgifNOqSDXJCPSo3t2VFG7ogPe0euH4Lj3fXIFbCMOlsk0/UEWYLjSyBIY5eG/wxgekdJPYAqtdIsoVp8lSZ7+XtsLK4oNjCeEHx1DCeQLD+Yexl72nPB9fX4nNq1s4gxpEc7a5sS/jx5PYhied93KKQhIUyxaqdAUDfFpoYOjH0DyepFREwpZxyBDFpy3xMmSMfb9QU+JX24NrFudM9fkkdX//ottwHjBmx0mMoBn6GbcEyDORe8f5rkMW+jAgmsqBKN6oi9+DWkB74g+WSAAQj4+DKGB36GbDWu6wpMXA4nDQXOgu6Y+e6t2YLrXDNX1aoX7X19g4+7QIBAbIoSCGzbAbqLXPLcdWpbnmtcZFe2mFzClJrK2ijGDbYhgvyM44KghJJXjCEJJlDJKjNFmuplBlnHYOXjVvQYJNNT3I+pO3ZcTgxOSLstE9qUbRipzkySQGERvsJWxBjAQf1mMzUBIdG5PVtNJpVRb5D0RTEIEuK+Ll+69KLL2DCZkOp5H95JQ6sQP+Q92VmG747XY3zUeqY4Oiy12kU3/v/IbYJX+fYzHBIzTUfh9y///OMSz9kScNpimoCrhh0jBy30qVPTwzoK752p46RPwenGYUr2m+UViKOrnc5Hq0666EtuuZdhowQmwQn/kU4C5E4r/SiMAGWtQylpjQrXbk8f2Xf9tsuPeHX/k0tUGmTZqWMCgQCvcR/XGww1GEmZK48WM3vXyniGefnHb4Borxm7JtYpHLu27hkc/02r7qFwkpKZT3jLoyYTgojM8iNEObswHPF3Pm3Cj5pSZT0iQWQxF/k3NMShd+U0eJBVx5MRmXqEHIOrpIOPgIKYlXTTCZrnBxWktE8nmrvDkKJtzpRW9YdPlIZ9kq1LwmSt5U4vry9hr/bSi8sDDhI//zRu8ekn84Jq/S2kmNOJXO6x63CuRRoVCUwCIfeDgMSSzFjYdroL61A6PUxhC9CVy/jF3I8Ozr8ijn+TdwxBuJf2u0idrmjYxGgsT6EIUzqFADcWxiQ5fQnAHvjWsM1cUSGGwZ5HbZMRJ/fVJID2VfLphk5O05IIQKlZKDHMs63MRCPNLCiA+j4Y3DpzmJXKhcY2QB5FpUXv9B/UTxhhZQj7Iu36QbCheKau0uZfE5UHl5UQrbLsF04el0pCMdTrEHIERJNKe+k4nAdpDAXGeaCFA21lYM+VmPnDxnHI4KxQxY0wM05vDZ0wI86MGKg35L1FTg+zFdQZQrMZg6SmtQHr4gq0bNEejmfz/0og5iGGpjkC8RYkVp++7mHAURFeqjPX+wuFFl2NN2oedgl9JpsSVG6ikFLpIq0vC6tdSIUqRAHJa3OwZ76G+SkrTDoY/skEXg+eTdKQr/BoBwapo6XsxjidRE7cBZeNiVcxWB6uW4I8rUGdjd82Y9i6BAJE9Gdy9A2daLrmnEyQ68JEewXYz8ZpYkmuEn40nBGFBEATUa5CdVEu2la0EjEVoMrYTeE3eNF9330SSB1gGQnt+6ZVtfG/Am/n0sO4ucId/bk7iPNCuqSA2gXs5ku6G94+59jPaFlmr8PbNwGAQ6HtTmtdQMFm77wp+O4YpITKYf1vVNHu0rgk5/QZ97mXV9DRgQsnWmcV0POLj1x0LrLPPI8YbWptIQd2xb7TfX+Fzby4smLZFtnfoFu6eERD7BtzyT9ITihmhyp7c1ScKKFFk6GBD63pvUK4lk6ZPdYz/2YWJrWr/QE34nIw3JSnRBWrw+sSkzNFuYVzvF9uUJR1utfIdcy3G3u/zQTOCpjYY+V1/aSJ4kEfRzT9WOF0jkW4HYJLfJu7m+jbGe4faRVO6ZryFe6h0RZLOlnRI4V7/Ve1PErnFmgXbMmKXQaGrf5YO7+g2OSptER8hGFmAQS/EtzUYIJ4OjVWNPsrgMuXtAyIogHSEwdlLekmdcW3v1GqxJXs632zx2NiilUi81CD2LlJ35w5jihf+dHTsCfsxtvE3WRBMHrBKPoiLinJ62/iXVd7xvmtAoMdRbjy3aAJ9K96pN7tXEnkzkOtSleN6fPYgh7b08iIDmdgl2UwxvkfIRiU4f/gFXAU+k0/ijDsaXrfqMF0q1uWmIjpXA7N4mfc/wfvrAAhsyg0iVy/Y6+izDQ18AIexYq3Na9EUsTbxuerZj0dJUcL7U+lcYddITly31edv69Op7S77t8YT2YxNr9uc8MjAEv1me38Wd3s1G9sv9KNllH23ITkBflR3n9Tr0lWHKNdGQsg4dglMNZuKvq2HncvLELQpoQx7izLokXGc+Nx79PNGV8EcC7xmEAuUTqu+eWVrLt4p3nzHEkEf8kxQMZ1I9KYyMXdIbQEDI+02ejWQldq6SgG9RsJlyQJ1f3nluf+qcjT4v1m8DV/luUMFs29RK5Bx0RPO+4fCGxApn8gnLiNVOTUeotci6+Rb9/q4Pcw7cxRY58YT+ismz6j3eyzx/r/sJJ2PXUH9DcwFj+SrcQUMWn6LJpEYkUMHsGX1kLnYa+Pq3095I0yK0Gw7PJtQ/Z/7lBJ1hy98y98zsiKgflrYjoVaG25/DLMeExZWFynVz2MXD0j6WGzDuooOVXu/KDy0b36qK+yzmBN4ixMyceNWoDww1lq2N1FbDtO6Ewww71KNbfmC2zIYm2hpSDNXt+UqYkYKHaMtpVRkoYuOPYl3fnRU1eUSEK/7Y7q7g+VacxpFwoyMslIumBwcum5/b4ToSAo9AEwbP2gQGELGudr92WSHuiOr5lnA4AvIhg1zzhSPljRFd3obtZQ8lde+rpLVul3+zzLP60SzN5wJ+G1/LVIdirC5KCGXgdyCbcCfuKJByDLWtEy535UyhehU2tVBSbR5DGivj+Rl6UDN3Hn0vDCuNAAqUUGkEObr454B3zO9Y4b2z6hbWOSk/8LnmcxmNF01MJMcfyE0hrwEe8MDDX97gwRcN7rKP5As0Q8+WVKt/Ot+Vt/EP+KiYAVgJzVZOjyMr62lrEwHY9bOk45OVecFQJOGent8B25Ln+HTH8fVJsynnPLyipdLNUOiUKm16oli7r1Jysl4nxISvBH2X6zJlDLiqf2NO871aQwMdkv0Guoap1K8X6TawBCjr6VXbKbF15N6wSEPoRC0oTzB0O0ismivRZYTV3qbsRgByveKpCyi/i5gXR+/22R0j+JUgfBWOxZHdLOvvh81utw0Ychv9MB6cQDmO08vWNtyU/69dI9sF5lgYw3/bcDzd7CamPLjanOe8hb0EpR5rJGkU/v9gBaLRBqKVEa9us0viKLlfeL4OEhhG8vaDlGmbPEJ+eDRVEN9ZTjz2TR3UZvfm4iF6p+6O758mV4XTO0D6BIPsS7Z1cylSzqKJa8f9gNaWd9Z3avtECwmm8EeYZP/916a+LikGCT+xxi99jm5s75OSSxkbfi4yoSHNBUWtzqdKsHHIfuHDB6bGGkyyrS+hxnEHYhZ3LINo8NOY6b1m/ci1/Tu9alFt34AN26xJks2c6II0T28DGIGHw/SWUNZt0+p9HE6c3i4djW6Vf5OMRTaxo/1Eq30NfxQmoliLvWAznfPfl1lJZXOeHylPiwN4RnvOEv5dLzJYujqCdTbJH3AarSZnHCTmhZA3FcA53TKpeqHHyXc57ZQa/t5wXRuJuOqxDXcfbvW7O3R0MrCIb6o2tM+o6H3alnl5NUEOm6tfhcPrTRyEZdbcxoYyOtTKiZmK9BSs7BQS21kFXHLPpLXquTkDISPv1p7qDi5xeQFqYc0UxUW63eJOuk+BE8YHOxRsGIWk5h7Ur/6a7CAS5mB2J16u6KF3z/mdc4HwUDwQqy1/GuHbqcEG4JPMV3Un4B+WAKUEG+zf/+fMPRY3UZ7S8fSb+A0HNwunv7kNertm81eSkuQbK3pbulQzKVJifpdOWgyK4ioG8tAY4kPZ01TbnMgmE85IUDYMGUZq2ta8UepA6RNyWOq5CLhbMzCms9xvtxlmhCt/toARdHtpOusyo1/wLPOo0bhnWIOgUNft3zxJ1U3b3BCPN7GOqrUkCXltuMBVJ1zX/MjzyufQHuAxySsowGQxL+BHYYfOehAOZrlnlUnnNI/TqLfWxHnWPJ5atnxIDSUhUcF6jW+NJtu85EB473lkcCvPp75v2KOyP4GAU1fQefiGFo6DEWoxzO+Wb1vsaPh3qraJAA5+WBal3bZa/uKGBJ0dJtkoRgdWV9O8ziKD7UbjEz8FBDEs2D4Vk6NapTKfAnnDmQReokbpDfMHvGOet1zwMr8C61ytTVUjzVWse/XuQh3pzByc/OwXg5QiPiZz/V4weL2Xmzjny0y/oMRZY3u9rP0Ci9CE5CqY+aVCEMy7m399F+GqVzGtVIICgJrTOYO36+hDmCav3o8K0fW4qMBOvT2O7l19xWHFVRHDQmf4Cuus+2sd8VYbzIxmQlBW6LLCf2RjCbhWhlzytR/5u0ZhbkyK+R8DAe4KLm7x6WNGQOnYkl5qXKScESP5Pj4eacKLCLvg7gSk+bFFoV4VECcCypnvDTSybwMyL3McH2FatGMkXxIQV2O2HMx34xix1MY83usNU//eYYanxK5i4RZrWZaRR+gOmFfAveJKnEqAeUi0c7EUF36ku8Ow4BZJzmwFJd33RKSz+3aP6i4QmQBuF++IDd/nTqHaIP8XouQNV9uEVEQYN1kAASToLkDzUY0jl0z9aKH8lliZ08LAdRCAgiObolYCzyAYBdZaf6S79AQOj18/scSWPiTAhrVIS+VqSvpX4806HuGYJdPbRPaHEaRhpeBiVpGBfLiugdh6/4YWmjFc3H1xWThWM57bJ5ljhAQ5FhkUI1XCA4nVvDP3RBwvcEgtYWl3hmpJMEHj6B6PTyGIqUXbzeBGkM70ZQY000EUUonlmQ8PvHJjGG+07dCHx/HIJbcQrbAmtdvgAjP0w7j6E8VJGtye2C51HWNvwy/2YixuuQi07BbU+4MQ3CdMzpOgGrpKb9DW/zwfuQ9VV7qKxUiXghPvPi0QjZz1ixrQOzzxD8FvAzFmFv/FdWTy1szAC2aHtHItBpCOk3RefXnEf06xBlYThdi20LWCHNHI7QU41ZH9TUeG7FxCFmJo4VyY7pV6/pXb8S9H2E3L1akF/JAAAJ2kGflUUVLJ9oRGOTFcwWqNXKPEOShO4DL4xsun8r4lL6Bve/TtxFc6DD8KEiUehzC29a6xkiGRVEBF8xJ829JIevXi8epQTHrf/UOuYwJkb8TtA/Mfyf+gaDkATfbrqZZEdWcHTrqLeMIkW+jDYxV3/df2k/XBXVhvuOI3JqzDZQ1XLI+xLCvtgfnjrolOpkWQKwIr9QfTpi0VDsNBLIExwgMa3pp57UazIxZSHEDJ3Du0xeeFF/NxzdtgOHOXQCxPdmZYV2O56CjiWFZ+LcI1I+Y4eu2HPNPDwkMvz36atTfGT6I6FKXIOEYyrpAmv9eSvuoaFOyTHG5l2SJZ7tbyQRNGK7wL7hakxmZT4uCK8OG9Zf6RqM5eN1lqqeCIqLDPU1/8VvCmd4hiiwor0YHEsBy1y/g4kG2nndDayVQpS9rCxoT+mdn3JlnefekTqn5WG3CRbJZ18vK9Oyz54RjzEppUiR297x2JorCRHfxwIGx0520iyKgr6sZSp8eCFm+HE9eqMrowlffaQHq97BeUHijBKl3oLTuolcXYZqTb8KRH0bam24k7CT2YKHgJpMetoPf9PYX3ySr6LdhAax+HzCLz4WRUoAYB0N3HWue9EsPJMH9JCAHhvrB54fb6Sz/0zUOPr4xble72NzblriHQKSh6DTZnEWtFpc+zXy1+44XOgFtWWtt2B4Zdk1bgLs0An4ap5gZ09mLBScTr5YEA3B9cgBwPhtUWpndiepZiing5RdiLQaB1/i3YJamY6UXNnRh5RhGgGtoPut6dgRdUqWJsmVxrA37171Dmwer8eJ8BxDrUZOBZ1DaSrako0AHlNZuQ8bapRIhPrrjTZWWmxojjvHUgRTVBAgE/SJBOx6Dq2qcHt7Sj9J/c6fHtq3E6Dct8I6LWT8X5pmK/DiWxXnaa3vU3l1ZTDIxjBNzx85Oi6yVMBWQGkOuy4q2iYVaTmdrNYgA+mZ6TY4YzJWFaEQCR90pKXbXE3Onsq2DljFbwReuOLDgpdxBhbcgjkYH0AP/Z3bmMKcRE0wykp7ijzzPdK1acSI30tZeSyCgEsvxuK3ijcncV1GkpWfTJmE9jikFGmKNgawT9NRFM/2aNgf1GIka3bRbWad1wYkzuElm2nCCZ63L2gsnZa58vRcCWRLDvOOOUVQMtthqSWxwpDbFhqGinXu9CCqo9gnpacfoJVmOjBM/oBixdP1OrC7KPYt2mKw75fi00av2usDMeX2NXzb9vyCeOxlopVY7wu+YXDDoi7TURFx3HNapiUOjoAeq/wxw1Ai9i+UPXDenP/jX431ofR1YmdKan5jd8O2zGCShWdoaTGgsEaV1Ir2LbpHmLJdIwRGrBRAWBaNvx5G+Vou6GMK5zJj+K1i7z3+So7RQRHPwVk69rbRpw9rtvhoQyjmROn2TmD4XiOPHpLTDPXl9NLf2IoLxoI79/GZFg90wg/pIr56UvVISaBm+8gJP4lvBluaZbh7VH2spOaS9DIKkRxWoNPL8KMkPUz4UhkyYh97+7g7v6A0LzU6Z0TAn+r8SKBbRA2+kp7Mn8dccpcfu9/X70nt+rRmyJ5M65dUMSyWtkBjLzlCRqNNNzs1P8zg8VeLVLxH0BfTrgWp1ypdWeXq6D2QrvlsJhf6SuHb6L1JkRtZcJu7z1V6bg9Hayd5IMDNCkeAl82cnG0ODtLbc28MbM8QELuagAHFMCPSb2gdnCbvYgaFW5PMDagYgsxkOZiawmJu6YUDKkUyXZkdM/gzMxphxqPPN7+FYCMgjWfTpYyPlD2yQ/U17tJuSslOsdDK5FKRo5mIL1nBW7thK8Hape+EjIGCipBosVq1C3zRA1xhSsDsu1i/NP0uvNHsyR4l2FsyxdPtIComaqmzIwnunOGiKHbtCHlcA2XFdO26oD2PBPaHoahNby4DYB2tIkj7GaDP9cXeIRAGVvzzoNcJDLTzeLTkzpYk8Beq/wcdvY0E3nvELp4gBw0LTR8fLQ8nyUNzpylzl2UFPVI8lPYzT4YFhAjnN/TgkDNy11KgW3NTrSFuOk8FSkk+zoMvMdXzF2WNuauQkaEqh7eZthmg5rGizY+cWkX81/WtxQBMFSzMP2S4CygsjiL9j3O2kUXdkc+jzwuv6CY4dacpdjjM5keiVdZZRftUiMBK1twaPslfC3pLYvA5Pjs0VXOMQvkIm7y1+T2iaQGp7edkhys8QRc+IMBfYGNUTmNOlMmqUXiFbH4GHsYGCTBHUGLgOJRRqA3eivIqT8gurIs42A1QLF6Flut4p8QFDesFVP/xCPjX2UsdM2QD4N86mpTkbi7b27hYaMYg3kHp2Yg1vvEGUQ4FziF+N5eKFPLhlJALQ6s1nEQMCWngULvLO/Ji6Yd9gGYsXjAoVcg+emPDMVDsNcZe/Z8fNn3c2bWfq+lLIlJhRoTff+LsoZpJv0jVczjgNT2uRXDr2eG4j5AzttU+AA5d93xc2zA6IDhwkPmvvln3U1Vrxb4u1StXsKgBYt6GQ/e90ad++vMqHITqOltUovUqBD+9m6bhEfRsuY3lhz3nK4GWphyQmQQIouZwlCq//DHRlN1QO4YWLNT0gpy6pl4H7GB5wuCK9UuMkJRnArKssvB4e5mMxft1kBtEFHGfzM7qUZX4Ulm1FHg3vPPN73d7WOpaZFEgS+4HtAuW1B0PftJ93CErYqYSc/CcSotXET76p013pR/9Kz1lJ82XzU6YgQsbT7ShCRyny1LWAfn6Yp8t0TM8C4Fujt8eeypHzeXiz1Wu49wLyoAu5pe/ywfgRv+0NdFnCCr+MicyOANAaUtmDP1lXBy4Ks1KX3LNN5kOMEKaVQddbZtcDxyxx7XyqdAXp8IWNCtQYFqVBD4PMSA4oDHsHrDC7YSKrNW0EcZSK0DwvMjlxknpicfWi8dT1SklHXTX+mzylhXODsiEtm4zUTxM+UvckykYTRhpPxrCN7BIlOWO9ynfSuR+qwxkRBdsnC7+CbrETTXlS2Zq0ig4fhhZ4jVCSihlPiH3xRGbffPg94WWuRAzhMYJbFpBHM8W1c86Sii/xiOqLti4Ww/kOMPaZW8LlamTr4ZFAo4TNR2wA8BXXRSb9R3k7y7bmS2pYfUCmVh/JCKVoc1qrB6hs62A2WwYRIRgAya1LM2CsYg46sPz1f5jovvvc8B+IbBbJh6n8zTsYMkRkpbqD8WIUxYFk+L28XzV/YEccDa76b1Gk+Sy3/EsZLpzWJZbqook5VwjK/vhNmr5nm1eTdr6Ifau0uewbw9CbjUa/i2NQDUtpcY+hk6dZNvXx5qo+hvpDdjYEGP5QWTP1YQrNFEfr2soL5y1YDCPu/qMx2hgjr9/bOpKb2ok00Um/0twAAAIPwGftmpN/29CaGpmer5CVDJkEg5GZgwUFnvogLemu3WrxECTwDefacogJxcJVUJ+IG14Zk1BlxSE3vCk6z4PoENNKRdvb6VLWEqj1w6sDA82c7Ic+WznzluoKNxIgKc+ETrP/BYohtV/9NBG2TzTwWcSpKj9ruf5U9ELQrBogXYlyoY1fT0TtsFfSLLE4Vwr0hg9zFo4v7Cc9sdIk6aj4BS9HGPR8vwanBg8vbvQ6uTmF9q4FOh+9XJtOjNgWQITkrS+sVu2gvnyIb7nbFZNNwhQPOAvV/yKLebg5//Yn2KViSxujlQ+UFpl7mVxzGq9wshgDGQy/0lCTkJ9TgYKIISnzmpmTtpLYxl0jlrjak2jgQMk6gUOgVEFSxIj6ZDVn1Z0mSzTKi6ymHUJcpKBbJ6Cb0mgjp+izDx4DAXNolR14TZm753DBcwTBzhCa9KuJuXVuQsUCpOMqLpGAkH2irvk7ob2VYYvZgVzVUJTC2aGF4MbGS/gcBeEz1KSQyYq3sYX/M+vF5xYX3F/SUfK6u5NywSSa2hRyBS2uR8uFR5zDaq/iopvj6zuJNL/9pSGEbiKVciW0TGSo9nSvQkf8FrwjTqbPuZ9w6keABLsWQRMEuiW+T4lOLMntJmt7+yXrxv8Idi8KANy+dJjXjkYkxDbNtZV5y/EI26nF0h6r3UgF8/70JyFbcFIQWIFkB8b7l4dX22GXsXaprugtX3OTeeRisJq/AN1Djh9Xm6tcJSFynWKBnp76Kv5xA21/BtLWcJI62a9MkVypcrhknCtZ9ZcZJHp1fySeUf67tbI5TknojhIC5KKgp/nrx7iXusMUjYxhcQlVoz6Lu/5yHIiq4QsTwhE+zmZG7heQOohShNAspk1QYdoipjU9dj5i+FgVOT/ZIDet0EPAwB7jCcrQ/v10Tx4temEe4vMIhRQ8WkPINkwXBr8RHeIeClYppvDrqFUvRnyWOkop8xKOSJCq5ChfccAgrCkPuiDjCDiLdv2dK1x6Q1792zG+9pS7Aq2DD4l5SKLzkTiA02f8BWq0HPFo5rwNe/Uy82/gExQrBAgnNWmPOgoonozJSmrbz0ycqwph7US4gvtWnH001HP8HIB85zdGtqooK8cazEHJ3du6+I8scRNgqynwycgIag59J/Sv3zpGBH3Ww23k6GvFy3A2PMzTerMm5JS56cVxngsOi4fO7x1uUbgNq7caXaAF43ZGA0MykJA+jRReyJO3iUcO9Vw0Ny4PngHlSJF6uHCLZ+c0c2eFl6ixbXpa6RsVDLu+jA1opdkqopmHDAUk4p3tbvKRXQroIahP9c5New17Dj4ROWaBRQovDhRevneKAQclXOX6bDX6R/II4ULc0R9z7HBQQnjaCi2rh7aYA/HRUsHS46wLBI7A59K/I9THF0y/0i5+B3qEO6bZnzOmXZm17j+KUH7/SnkBrdQn2JrS+kD49CyClWa5SvgwhuPe8GT7m6vfKpA7JuXxqARnVEn9eBPjw1T1gABwqKM3r4ERCNh1h8qT9H0dwgICu2H1HIJVuJGLvU6PG/27nbOossDOXIBlndLy98qOaH+FZ48GWhm0kErg1CH2QruIwy2s93WvxJjzthYnrG5XJ3RDTPxv3AV7ujoVXc0qXduHSslSsyQKQVxZYRSZaJqdiDB0zhG6OnBtNtz453Xg5HS55gh+O4c7v2KGXrpNHGOZVM0scwh0aT6VsX6v7ZP4n10ywW3eLc+OSs3dIIsIwo3NS+dpvNlDlZ6a6Po/phgqUaL1LtnRnbf0tfcZL83KzXyeT2bd6eX/9nLggPly/LKz5srC0cvVJoJt1qwXqYo6QqUNAklxJ39AF/lPrGu41fc2dDtgldjVN/54k+f5tngpylEKngLy+uCmOZIziJA/hE4OvwPH+nq9oZWp7iNU0wNVIQ+ljIICLx1wvqzp0v9hTSXu3eD7J6sPfnHWS+E6ik98I6ZowiyaQW8jnnQau2cfMajGTGaTF4uD4H9D2te2/dORy+WstN/eB4yD1+ljkNLqsQrt1cTucfjPHLtSZ1QqueQRRnkawxjujAEvdU1ji42VLUmnyCDPaX3INNQkzxz0EKa2E5W5CdwgjWjCzwNPZJSpkjRgvg4mFmGvky5IEYG2iQtUldgHkXdPllY5Zb/zVQ2D9dhtN3OYUNH+QawlFKXmKex8HiK8/70M7OCHy/OzMRwn4AERRa7T1QQSgtzORHg1ivDMAtek5vzSyIRB3PUtxZHyuz9ePL4ZqJl8C31fvDXmw2rWdij8CL64LqB910zJsMB1dUNu5t6/JdTByW/12rBiWe0bjwV3Js1v77XeMWtqIPKRvKotsdRL0gAofP1Wsw2zvbcWrY+Mxeu5efPjeEPtH6O7/2B/XnK2pwv7WA5n5WYYGH+OkTZzN3PyunxBJPgOidWTOPzIg+crRGVW/Di6fvOMkz1gTjyxRbZcLiwfVQZjBKZ4OAeLUaq9qrp1HxRc7BBgj3cMremiyHgQZTP6UUw2z1o+eeZbBmRSHEpbmtLwR8PtskpvfoPY+XVxFK9+ip4Sxd+u1q9gVHm6c2feTxysHp2Q89nAQCdGBBdzuQE9lDwXX67OHPIVLdqeNUZg9p1wWusjn2p4rHzojNvB4BiOLme6/XSiz/aERP5P5Djwc8YiCHRQcgFGdyqFcL8kdxGwSWW2+MJ9ZtfLccu87YLf7O/OXQErYa00Px4+isdKqTNRNpQhzmSmQaJldrqcMeGFMwIpm/4YjxBjQwRM1s/XMNIATbkhM6peqagsXJ2g9qcaNG/ubABO0iL6vWtoOygOII98FRHAAAO0EGbuUmoQWyZTBRM3w4d0b1IMFey9PqVZoyWY+qEKjXGVwKo8lN46fFX7HFGFE/bkhlWKbxkBciZ7yTpTRr1Fd7gc0wJ4kbz9Mr/ZHKpGoVl40c0Gz3cyDWLaHojuvw5YF6tugDcJ+HqTyQGn7xhirnea8mu1lcKVx6uYQ2isBE7kbuEEle8VgRo4mkL5v2ySpGNER5tluv4GOlnriB0HVMsQd/FjCKSC5ERXrYEF9nxrfKHEMpp6tVwpNlWhCNw6SOB/MefpQIauwhn7wy0Q6BG2oo+G/2rvcI1s+BgJX8aUV03ybwmalDPADJYqGS4B+UyBrW9bvLIr7GzYsxyDeE58b9KNUIBPl09jCo2RquKI5jF9bZWWRDE/Eqltq73TfG33cO/x8FR56H/nKzxH0JoEpPQjNMfITIrJ2EKFtDsjufejNdp0gQugGxNzKD+xnhbqPRMLx5ipScuaFtbRmcmjbpSYvwBLoI2CxolJ0ljBWidR3J+6QNouSinLKCO6+AbZrtqLcRnR21h2uLxJ2RxVpBOmPW3pgZRjEGVoBc1rYUEwuB/iZQ81pKpxo694SimCpO4oyfbvSxNICEwXQWmpeptTDtPJxYKlEGb3GORBGkgjGftDQPX4Z+ez2QjFhZTILbwzynfOuKT+seGo96W7fY9IqBE/ls6lhGXZXDIFznG7AbS3L/z7N38erAdcXjQ3V4m1EI+vwoKiVa0mTG2YBNOuv89c72iBwmbiakAE7lZUWmRXLgzY4cs9Y4yM5rKIYPgE9h3yxsAmFEjlQWAo6i1eMgChnU94SXkLGhhU4Nlgoq3kmxRkriiH2XtaRwexJQVwzOpcP/IkgKLubqgrvdb2bPppYqvOOSRRnsvNJr03WqVoPxS5egvEmlhadkpgG0uHSCoug5vWVy5c1T9xGr+7wTbjKZYxphLsTxnC6UtNNKXYginLJxm2fgtgSH8CN6KVBDrzsWEe6sQ1bQst64TWVaoz/hUme9FqzofloKx9TXorCqbh8sVQWTqceo3GK4pqqyMFHhaxqhSlqo7E3uDufa63huUVrPWabQpFJSfF3X+Fmc0WPi2eAB0ej2fmaAlRh43gvFQ4ij6rFnfn/74rfaW0mFq81HQA++mTZ5yaSAWZmC89oOkOAIkp5bDe2DugD8xBb5FXkrsJu7tb2IQTaBL5lh+euGzHM+ziGoEUQJ/PGpArWECjl36s3rRBaNKEg7YnXbSekJkGpu0p8lNFTj3ZzbKHllJ3VYHiP4b2JM6DE8Uw59VMX0Bjd+kRL7c3zf9xuGM8ZV+T+M392IpQSDd048O67WayUhovj7CRMFPfl/Kvxf4xtE/m7prW/zuaZrR3ccwc0UKGHejLstAGv53yd3U6X3t85G/cFDmyrduEgSI4ttj3GCjrqt7DoVRqy/KsKy+p/5j/DRqgNARhEl+CXqwypvf6yLcIxt6wiFvIjRT7OJna2/XgRGe4K4pYDfiTfy+MtUBh/zsTQZdyBSUd0w/M6OrqRotRL08msbFpPGPivDGKPWpGaDnDSfHFbeJKWZW9fMVZCwvjoLx0/RB/I4IJRreX+nus8HJSX1v4PbAEOlupTVTNu0BW1SRO+6DWcyhdg3Q+fGxHnSYQdhnCJNCZJ3G7RcO0CWQkElVlYL5bqSDdGiKh8PmBlFofXlAS0jJ0st+8WJHVGmZe8Awyf6pzWjW3R7+wi3n6fjQQyp0zzDcW04C7KYnwEE5noWb6t8CJfK1IlgRmojuSef5/vvf2MT/yr69Guf23VxcNG57l/ox8yr7j5zw13tZkFv5MgUlsYUiRon6IK1Y/24GHTlTVQBPmVoGFo3qGVVBFj68BpFTsqEG1BEps/DAWSiLtEVF4nTDuqwOuq1LlObw114EYiZRvcI3Yi2FwxA7LqV0D38/cbqapxXse4NUXcu/+nh2EMyKjuW9IDhbMLJlIiic8b8YB+2EAUI91e9F1s39kueqRf1x7N8Voshd/lDIQlXjqgf+HQMH7LcTr5ZQs0xhawyoZFh481XxZ5Srx/wbZPh+bCftT/6OCnq3jb+/6EVnUJpWQxoXSKSj3DJJT5Dot52U4da9ainTfZPzoQqhk4t88XSefo+nQX3L5T6tQ0RsuLXauo2uTHGODcewz6gAcaIOihza0nJ3yNCzJrC0m9vUI+BilHg460RdQXqfHazvnFe4E23f3LSST0jaKI/ga8bhStb0yVRO8VId4sNnYLr7o/talds0gC3xG3nCd/uwLhEpFxPV+cuXJW+eCOUpZ450BJNVLJtPf/v7Vxw0GF7wb+gvZwAKKYpgacgpikPLqokAw+bPu/gS/DTWYtjf6jjP24hDtej0/EAPJnzXoh+DEZcpy+kBiPIw69SPqor2VVtHSSGIKVqHZz/OYqVIXbFKLang7hRVuxDc8dLmsvQpIyA1+Ah8qbS3AVbGnFvaIc1tuTE6EWQQCDgU2351YTbIMqgXTClaepcpZELbqDHdrbgZQtFlC419IF8qz5A45VX+CR/VWK9Se1PLwz4ukhof5JZTo0ygyQwD6UIA4lY7MJ/QNnpPzD4DpcvvDnjVqHjnXVL55k7AqPAXWtFvl9dpZjJpSXpxZdgw2W0tGM2e7bnoYdTDtc8a4QFfiDvWsxfnmDr6z4EET/bhjGvpqXhXbAIF5NkmjsSBMwEyjD8KwoJYhhpLmRztAU70MQ/RN7AOexOh+OgAcVd+ViS0AhXTfl7VnY+ZbAGO+om5ogDqOgfzmir5e4Uk9ITFusBROiyD1moTg4i3oSJSCQEzn4S6PLz/UltCIirZa52mfXoy9xRXin41E0Nt82rfjpp4aGAOn3Dc5p0xF3ZZfeL4WXn7PRwkuPzXObyBs0xwf6ZCoflBDBI7bQkv0Rb27AiKtBiU1AcfGud5S21r8q/PaB6ZLT/YQPZZXbfEHJtNjXSKeF3ldjryiI/A52MbYhcpCbD4U3izGEZPowOaaN3J5OchhooPcdcM9IKo4JzeodOpKzH29kFbr3xPxXnd+U7d4FF2OT3hNQQWDRVqEaz7mTP6BFrOU1gWK0qg/j608i3Xw4mTBG4OOiMgDV9VpGjgGkDiHSChwS3eY57zqmjj6DWkuaS/VAiwM55COFx11XdMDPUhRI0Q+FYh9rTuMJoVPFzW1o78+ltSvXM+OrgsGrLeYyDhKwaEWB7R14Q8vPfuAYp0SizRPOZALXdBYgiNVbN77ZFyGSosPWutRprGWBk/IX6ukzJeiegBEDNjxNZUCYHmFs47Ko0D2ucuSJ/9wVJ7VAGvCWiMTwAVBsChaigKxPTlvnPXWQqtqlrmcGA6qXHTxdUlYT9aHaX9/zXsCeUfpHslWHagAm3t8WNtMjJ5W//IRt/Mv7IC9DIzJFLTggq/86Pw5HtZ9QE+I8ibprkR0ZW0p8A+IphRriwtEXHccDsLuIfzmVkOyspGiuSBq29sc/E8C07qIADt2lpwQ5qzI1IBC6Ity5qUM8QDmoc4X17qMb2FaGMMeANnZxIgEB1djfj9ArpH8SvkFXSimDLPl50UmC9UFZyMr90RbZIKUqnwP5BtDgKzqYp3HJD8MFnEk7zvHs+W7ycIhevWi/cLuhkehPZSbjaK/Rv0nNdK+F3wTZD9eraGLw8WiR5MhdZ7bFUAdWo27XAQq+yBcz4V3aQrs3XyDo8nepxZBQ3AcMRaS9pGXd9P3lX7tE9L42VVGQC/Jy04iXjJ1fPK8me7gGWSl0Ib7hbpdJqY8+nMAZm7L+EqRvtd1TBj+Arw0dR4gjWHmxpSUzS3qoAEVhw1plG0FbwgPgeJdipS5pX04QTjjnSmbt5cYsI+dxBvGhIpVcooiKSvOP1PsaDZp9CRl0GrWQGJiZYYCqoJlUkjq1GUTiUVK5fvuR9T+z4IQbyYcqCGWHYLByVf3elxWGow4/+1A+/eVHN5V8Ce5uWhNW6dVwbo2vnYAIh3g4tzSKxgl36uutZ9Q1qanD7erKCaLcLdv7JMIqSOmRhm9rFMLCTFeFG5F3pBbbBwaS4j6FP0H96u2BoK0SlL4O0tJieSjQAAyYv54RoPIfNtXkodQb/KKoD0wH/VdNNf8jDCWIeEvSElcrZNClESaqVXbF3q4WwgYphn0sl3QOsz189ybSwmc8u5m1l7p7zYRNb4Q6IiyInQpWKdiIXn6VQzOkDfjAA3ar2APfb8LvUsJA+K0FKXrfD99b/Dsgx6pEU9IxELfHuZkugCD8JedFJw3H8SCcGzX3BC+tHHTIXnk1OSWI2tZWkXkbkLqoV+VEwh5bzyv2asDM7Xlf6exc7UVHriOQMxTt3lJj6HXEsWMYCD0NccGro5Swe6xIBDOPHse0pmgNtjMpHKK8DAQF7LLcTTkvRiuDCHZJgrLBFB6EV5IDMytUXXaPy0kR2P+130vQoMjwpFtLqCXDSITUlC+/z+Mgin7CM0Pkh8S4cmbV6CvI+N0cEPsQaGhkzG4Mu4z2n2vdGZUUgosoZqTWd4Bsrm2QqAGWzPPANI5BCo7aIRGSnwFm5toR+yPhJg8ZgE7Iel23NnOmVyrnIPzqQq8YoVLwInrEYLAfyW8U1FXE7HhRExj/k0Ec8SGQ3rOXZfb6rcw2foo+ufaSg1LhUB1Ig2DRNT4E2g6pHX1mrOW29J1n1D+HhA+AgmXXkG74E5XvK2Rc7Zrr0sxrGBYyEeO9YqJXDXNWwclEoOsqHopLjPzPaRrVtf0TQvW8n4dsT8zKJR5Cd1IR/N6uzLhreWKnwGox7gnSDHYzYOWj7I2cLEOcO9NX2E1SPQbZPozUHuMd5eEwRNMo4ESMiuj/unMEhM6JZ0iQVbPUW1juswaRCHwoUPKSToeykcgFhoH9KtpCG3S3gdDbHbKnE46SXRlaGA4N9ZGo1sX+mKqed19JXvXt2zvFV14ijPBFN7w9lAskj483JDbr9yUOVAiO8uLUz8Ym/L/LtppAX+qmS1KnIcFddQYmyzLtE2JcZPDX2vSNH/xC03QLUJyMzvI10L43J5ZHONjhzqlWZfpFabtATp5cjXWR6JlkCfao1L1tUaqVnnHwAACIABn9hqTf9xA+SjOfcrBH8ImPH7YAz1PyFpbXPmjKlKroyiO9dpxpj34XeNVi8fQUnnfLXPWg3w0SVEqumNUs3ULvCcPC+BnROBUASgCtp0uvGJd9n/LhGuGPfyz5vvFXV6nabEJ9OdpTkH8dgz3w+fXuBpOitP7+4sM/ql1LMMDkNFmbXOv8qWovJCTYyEDqY4KM7lPsIAZwwlogRYeDDkydI02iP6aNG+V1JcvqoFTuUwjavcyobtPCxcv8UQNDak4EmBN2slL51xP8kcXO0uRy2okzbYFSUJL8LkN2acn8//rep6BT7wLBkG89GBuUaMsB+xNCjzrdnpmHYYJnsTtmsl0BpubJp4SlEtyNwpvlw0/nZoAHHovR2KacCvJ3mk/9co33BIK+SfrGmke08AyCJZGGWnLjG/xnaHznriO/3ycjORrcJYobv3QVx8NEtQt5xHLFhrbZUMBcEN5c/fhiKaVZl6KlHLuPCigDIVBZz71yGvyCfMRf6VMzl1r/QfqnJD0/y8w8694psdu88vuzLszlqWlhyJkiKKn3Gh8sQCOlMg7nwaZVtAVcppmxeLJAvCvq8/JZaLNBuuKRUcecSXj7fWeKnDuJuDBe3nN4Hnc8d6/816QdfZ0frpJHj1INiOHPYDxF5tG01AjofP61E7ljqfC4d/ekCcXxuuFgmGzWI2DR3ST0kDFOSG/0vNlVDByQdbez9eAKWfq2rrpbSzZCmtLLqFgfz9pXeqC/OZkkQoNlpMZNQ3D6dk2ltKibaPU9RUvy4+/8calMcBe83TB4h06JBpI/xoDjpFoj7sx12/2Ok1h+UCYFA8a1TMQN3WfACbAtSfNqGQPIwpMtSwUhIslVcAz/55/tIX76eXWnx8xZekNAI3/DJAYQCPjQHwcxsLC34HJEAn4A0ahnRiSRnjduLc3z3ua0plEi9MtEknWeDlhUUsTcdR+zTgwwlKNbdNvx3J693p/LlYjxfwzZSbZw/b7rnrW+JBVFNUXooj5RzeOhw41hEYThA6sLscIvhdd8qsnNGGcTPEYOEg9lhWnq0OWle5mdSB3PT5dxw7pG64VAp5MFrziNdhu8hcAoodGvHY8AMTdjq+wq03qv6MLJDWK9pkIvBuHbugFLsn8WfiGwQf3hHJq9rwmeGSrcuqTeB0aGeWXj2yUF2/UpvaA+6ZkbMZGFDIBQP/IiR8sZMr+XgD+1kRTXDpHRcMYVdx8OCp9Lw+2TzOUDGRMTtvIdTul2ukSn4gBfIAIDQy9VUCtXnv9fRwlotm+peJVrAmqny1IrZ6QtIHueVM0tXojwpXJPjUPEV9lk5ZH7prTpUu/wYs4wWF8ui/lwIgor1ISkPb187zMwpw0FI6eraDAgBZnkzpeqEbiURtdzxYTfr97s+h98kmuMXmgqGr3p5Z+lGzZskp4p5guGMYT0xb6NOVeGLiwQhpq3HkUoJMmfdThQkLPPvj6VjDfYcR8YcDaXjt8dZZFZmGYbGAbNc/zLAQSu4KiToZ2SMaZt31/hJ2pElOV/MY+bA9msnM6hb8xqQQK7/r26S5RUaecJ7WVND60fcDZjfkBKJ8io+ZqXKS1yGbdEGzrKDslXsJEZXCOyA9Sbhk87SfTl2vRX9fG1EG+7obYHLsPX8Oj4KsMHWxWLxyptZ0F9CCK9IqMI+5/wxkVCwpTmzwq/qbrMTkNndMSv4xZJXM6GoL2BfB8gTHE6HEwx7fr15LLbfFTNioga6muxUDP5USbonw1nf1sQgBqnb21dasVx/w0SzUIqPjOVqp0TW3TjZmLn6VOCwM4wgTumpJ2az0aCfZ6ZPy3f07DpQL/8NJWBA5M+yfOIFLGrRAhok+PXXR5ALeuHGCjUeW79IPosbC8TidrUJQkwpdmg4oKHj8CVL7Foq5AqCXRlVI9fSsuLertzVv9QZdxRo80Pn4agBe8PPmmzOvQK7m/bhIfsGnYq0VX10FHSlFiJ7NhweGhupD/ei5JPWZp3frVTo+5syO1RmwOQcZSk8xpiMCLzehu9P7+v/+X31AFw1O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type=\"video/mp4\">\n",
              " Your browser does not support the video tag.\n",
              " </video>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "from IPython.display import Video\n",
        "Video(\"./logs/eval_videos/2.mp4\", embed=True) # Watch one of the replays"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFT6tkeR7mfU"
      },
      "source": [
        "## 2 Visual RL\n",
        "\n",
        "In Visual RL, the agent's observations are now visual, whether its an RGBD image or a point cloud. Vision based policies learn to extract task-oriented representation, but are more difficult to train and need more work. Section 2 here will cover the necessary steps to get a basic visual RL agent working on the LiftCube environment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LofwfGDlPavE"
      },
      "source": [
        "First, we note that SB3 won't work out of the box due to observations including depth data as well as being in a format different to what SB3 requires. To remind ourselves, lets create an environment and inspect the observations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "NNT4Fi6ZPTa4",
        "outputId": "6c60fc7f-2ee4-4e4a-e401-c306c3a0aea2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "env_id = \"LiftCube-v0\"\n",
        "obs_mode = \"rgbd\"\n",
        "control_mode = \"pd_ee_delta_pose\"\n",
        "reward_mode = \"normalized_dense\" # this the default reward mode which is a dense reward scaled to [0, 1]\n",
        "# create our environment with our configs and then reset to a clean state\n",
        "env = gym.make(env_id, obs_mode=obs_mode, reward_mode=reward_mode, control_mode=control_mode, render_mode=\"cameras\")\n",
        "obs, _ = env.reset()\n",
        "\n",
        "# take a look at the current state\n",
        "img = env.unwrapped.render_cameras()\n",
        "plt.imshow(img)\n",
        "env.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ih_PagxUPe8Q",
        "outputId": "58a0c50b-c7bd-4950-898a-1f189d6701d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The raw observation odict_keys(['agent', 'extra', 'camera_param', 'image'])\n",
            "The data in the observation:\n",
            "image odict_keys(['base_camera', 'hand_camera'])\n",
            "agent odict_keys(['qpos', 'qvel', 'base_pose'])\n",
            "extra odict_keys(['tcp_pose'])\n"
          ]
        }
      ],
      "source": [
        "# the observations\n",
        "print(\"The raw observation\", obs.keys())\n",
        "print(\"The data in the observation:\")\n",
        "print(\"image\", obs[\"image\"].keys())\n",
        "print(\"agent\", obs[\"agent\"].keys())\n",
        "print(\"extra\", obs[\"extra\"].keys())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tF-xYcxqmkC9"
      },
      "source": [
        "There are quite a few things, all of which are important for solving the robotics environments! For in-depth details on the exact data stored here, see the docs: https://haosulab.github.io/ManiSkill2/concepts/observation.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PpJdClsNmz4o"
      },
      "source": [
        "### 2.1 Vectorized Environments on the GPU\n",
        "\n",
        "For learning and evaluation, vectorized environments enable you to take actions and receive observations in parallel and accelerating everything. As a result, vectorized environments are critical for fast reinforcement learning.\n",
        "\n",
        "We provide a custom `VecEnv` object and `make_vec_env` function to additionally optimize this vectorization onto the GPU. Note that this is different from the typical `SubProcEnv` used by libraries such as SB3 which do not have a GPU optimization.\n",
        "\n",
        "For vectorized environments that return visual observations, these observations are kept on the GPU as PyTorch cuda tensors as shown below and now have an extra batch dimension\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTh2plX2mzOF",
        "outputId": "7f2c9174-2b7b-4719-f40e-24bc792bd8ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m2023-08-17 22:35:52,404 - mani_skill2 - INFO - RenderServer is running at: localhost:37859\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Base Camera RGB: torch.Size([2, 128, 128, 3])\n",
            "Base Camera RGB device: cuda:0\n"
          ]
        }
      ],
      "source": [
        "from mani_skill2.vector import VecEnv, make as make_vec_env\n",
        "num_envs = 2 # recommended value for Google Colab. If you have more cores and a more powerful GPU you can increase this\n",
        "env: VecEnv = make_vec_env(\n",
        "    env_id,\n",
        "    num_envs,\n",
        "    obs_mode=obs_mode,\n",
        "    reward_mode=reward_mode,\n",
        "    control_mode=control_mode,\n",
        ")\n",
        "obs, _ = env.reset(seed=0)\n",
        "print(\"Base Camera RGB:\", obs['image']['base_camera']['rgb'].shape)\n",
        "print(\"Base Camera RGB device:\", obs['image']['base_camera']['rgb'].device)\n",
        "env.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While observations are kept as PyTorch cuda tensors, jax tensors are also supported and with minimal overhead you can change the cuda tensors to jax tensors using the code below\n",
        "\n",
        "```python\n",
        "import jax\n",
        "import jax.dlpack\n",
        "import torch\n",
        "import torch.utils.dlpack\n",
        "\n",
        "def jax_to_torch(x):\n",
        "    return torch.utils.dlpack.from_dlpack(jax.dlpack.to_dlpack(x))\n",
        "def torch_to_jax(x):\n",
        "    return jax.dlpack.from_dlpack(torch.utils.dlpack.to_dlpack(x))\n",
        "\n",
        "jax_obs = torch_to_jax(obs)\n",
        "```"
      ],
      "metadata": {
        "id": "mZovQ8j9Ey_V"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdnjOKZ5ml3I"
      },
      "source": [
        "### 2.2 Adapting ManiSkill environments for Stable Baselines with an Observation Wrapper\n",
        "\n",
        "If you look at the observation space and the returned observation, you will notice that it is a nested dictionary with different values in there, including both image data from cameras and state data about the robot. If you use the GPU optimized VecEnv then some of it is on the GPU (SB3 expects numpy observations)\n",
        "\n",
        "SB3 won't be able to use this out of the box so we can define a custom observation wrapper to make the ManiSkill environment conform with SB3. Here, we are simply going to take the two RGB images, two depth images from both cameras (base camera and hand camera) and the state data and create a workable observation for SB3.\n",
        "\n",
        "Feel free to the use the code as is and skip this section. If you want additional customization such as using robot segmentation data, point cloud data etc., you will need to edit the below wrapper appropriately."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "G8dAIBkMPdoJ"
      },
      "outputs": [],
      "source": [
        "from mani_skill2.vector.vec_env import VecEnvObservationWrapper\n",
        "from mani_skill2.utils.common import flatten_dict_space_keys, flatten_state_dict\n",
        "\n",
        "class ManiSkillRGBDWrapper(gym.ObservationWrapper):\n",
        "    def __init__(self, env):\n",
        "        super().__init__(env)\n",
        "        assert env.obs_mode == \"rgbd\"\n",
        "        self.observation_space = self.init_observation_space(env.observation_space)\n",
        "\n",
        "    @staticmethod\n",
        "    def init_observation_space(obs_space: spaces.Dict):\n",
        "        # States include robot proprioception (agent) and task information (extra)\n",
        "        # NOTE: SB3 does not support nested observation spaces, so we convert them to flat spaces\n",
        "        state_spaces = []\n",
        "        state_spaces.extend(flatten_dict_space_keys(obs_space[\"agent\"]).spaces.values())\n",
        "        state_spaces.extend(flatten_dict_space_keys(obs_space[\"extra\"]).spaces.values())\n",
        "        # Concatenate all the state spaces\n",
        "        state_size = sum([space.shape[0] for space in state_spaces])\n",
        "        state_space = spaces.Box(-np.inf, np.inf, shape=(state_size,))\n",
        "\n",
        "        # Concatenate all the image spaces\n",
        "        image_shapes = []\n",
        "        for cam_uid in obs_space[\"image\"]:\n",
        "            cam_space = obs_space[\"image\"][cam_uid]\n",
        "            image_shapes.append(cam_space[\"rgb\"].shape)\n",
        "            image_shapes.append(cam_space[\"depth\"].shape)\n",
        "        image_shapes = np.array(image_shapes)\n",
        "        assert np.all(image_shapes[0, :2] == image_shapes[:, :2]), image_shapes\n",
        "        h, w = image_shapes[0, :2]\n",
        "        c = image_shapes[:, 2].sum(0)\n",
        "        rgbd_space = spaces.Box(0, np.inf, shape=(h, w, c))\n",
        "\n",
        "        # Create the new observation space\n",
        "        return spaces.Dict({\"rgbd\": rgbd_space, \"state\": state_space})\n",
        "\n",
        "    @staticmethod\n",
        "    def convert_observation(observation):\n",
        "        # Process images. RGB is normalized to [0, 1].\n",
        "        images = []\n",
        "        for cam_uid, cam_obs in observation[\"image\"].items():\n",
        "            rgb = cam_obs[\"rgb\"] / 255.0\n",
        "            depth = cam_obs[\"depth\"]\n",
        "\n",
        "            # NOTE: SB3 does not support GPU tensors, so we transfer them to CPU.\n",
        "            # For other RL frameworks that natively support GPU tensors, this step is not necessary.\n",
        "            if isinstance(rgb, th.Tensor):\n",
        "                rgb = rgb.to(device=\"cpu\", non_blocking=True)\n",
        "            if isinstance(depth, th.Tensor):\n",
        "                depth = depth.to(device=\"cpu\", non_blocking=True)\n",
        "\n",
        "            images.append(rgb)\n",
        "            images.append(depth)\n",
        "\n",
        "        # Concatenate all the images\n",
        "        rgbd = np.concatenate(images, axis=-1)\n",
        "\n",
        "        # Concatenate all the states\n",
        "        state = np.hstack(\n",
        "            [\n",
        "                flatten_state_dict(observation[\"agent\"]),\n",
        "                flatten_state_dict(observation[\"extra\"]),\n",
        "            ]\n",
        "        )\n",
        "\n",
        "        return dict(rgbd=rgbd, state=state)\n",
        "\n",
        "    def observation(self, observation):\n",
        "        return self.convert_observation(observation)\n",
        "\n",
        "# We separately define an VecEnv observation wrapper for the ManiSkill VecEnv\n",
        "# as the gpu optimization makes it incompatible with the SB3 wrapper\n",
        "class ManiSkillRGBDVecEnvWrapper(VecEnvObservationWrapper):\n",
        "    def __init__(self, env):\n",
        "\n",
        "        assert env.obs_mode == \"rgbd\"\n",
        "        # we simply define the single env observation space. The inherited wrapper automatically computes the batched version\n",
        "        single_observation_space = ManiSkillRGBDWrapper.init_observation_space(\n",
        "            env.single_observation_space\n",
        "        )\n",
        "        super().__init__(env, single_observation_space)\n",
        "\n",
        "    def observation(self, observation):\n",
        "        return ManiSkillRGBDWrapper.convert_observation(observation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3r7hmN2inRSM"
      },
      "source": [
        "We can now wrap the original environment and we'll see the returned observations are now more compact and importantly usable by SB3. Note that the rgbd information is now a numpy array as opposed to a PyTorch tensor on the GPU as we converted with the wrapper for SB3. For faster RL, you can use an RL library that supports observations on the GPU like [RL-Games](https://github.com/Denys88/rl_games) or our own library [ManiSkill2-Learn](https://github.com/haosulab/ManiSkill2-Learn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlNEXHV9S2db",
        "outputId": "9a5c7e78-b4be-4a4b-9a5a-a169dfee2aa3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m2023-08-17 22:36:30,357 - mani_skill2 - INFO - RenderServer is running at: localhost:59683\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['rgbd', 'state'])\n",
            "rgbd shape (2, 128, 128, 8)\n",
            "rgbd type <class 'numpy.ndarray'>\n",
            "state shape (2, 32)\n"
          ]
        }
      ],
      "source": [
        "num_envs = 2\n",
        "env: VecEnv = make_vec_env(\n",
        "    env_id,\n",
        "    num_envs,\n",
        "    obs_mode=obs_mode,\n",
        "    control_mode=control_mode\n",
        ")\n",
        "# use the VecEnvWrapper we created earlier for RGBD data\n",
        "env = ManiSkillRGBDVecEnvWrapper(env)\n",
        "obs, _ = env.reset(seed=0)\n",
        "print(obs.keys())\n",
        "print(\"rgbd shape\", obs[\"rgbd\"].shape)\n",
        "print(\"rgbd type\", type(obs[\"rgbd\"]))\n",
        "print(\"state shape\", obs[\"state\"].shape)\n",
        "env.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNF39XEAnVlm"
      },
      "source": [
        "### 2.3 Creating a model to process RGBD and State data\n",
        "\n",
        "SB3 natively doesn't support processing RGB data with depth information, so we will need to create a custom network to process that data. We can make use of the SB3 BaseExtractor class to do this so we can fit our model into any of SB3's algorithms. For more details on feature extractors see the SB3 docs: https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html#custom-feature-extractor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "SsgrC7ocnUye"
      },
      "outputs": [],
      "source": [
        "from stable_baselines3.common.torch_layers import BaseFeaturesExtractor\n",
        "class CustomExtractor(BaseFeaturesExtractor):\n",
        "    def __init__(self, observation_space: gym.spaces.Dict):\n",
        "        super().__init__(observation_space, features_dim=1)\n",
        "\n",
        "        extractors = {}\n",
        "\n",
        "        total_concat_size = 0\n",
        "        feature_size = 256\n",
        "\n",
        "        for key, subspace in observation_space.spaces.items():\n",
        "            # We go through all subspaces in the observation space.\n",
        "            # We know there will only be \"rgbd\" and \"state\", so we handle those below\n",
        "            if key == \"rgbd\":\n",
        "                # here we use a NatureCNN architecture to process images, but any architecture is permissble here\n",
        "                in_channels = subspace.shape[-1]\n",
        "                cnn = nn.Sequential(\n",
        "                    nn.Conv2d(in_channels=in_channels, out_channels=32, kernel_size=8, stride=4, padding=0),\n",
        "                    nn.ReLU(),\n",
        "                    nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2, padding=0),\n",
        "                    nn.ReLU(),\n",
        "                    nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=0),\n",
        "                    nn.ReLU(),\n",
        "                    nn.Flatten()\n",
        "                )\n",
        "\n",
        "                # to easily figure out the dimensions after flattening, we pass a test tensor\n",
        "                test_tensor = th.zeros([subspace.shape[2], subspace.shape[0], subspace.shape[1]])\n",
        "                with th.no_grad():\n",
        "                    n_flatten = cnn(test_tensor[None]).shape[1]\n",
        "                fc = nn.Sequential(nn.Linear(n_flatten, feature_size), nn.ReLU())\n",
        "                extractors[\"rgbd\"] = nn.Sequential(cnn, fc)\n",
        "                total_concat_size += feature_size\n",
        "            elif key == \"state\":\n",
        "                # for state data we simply pass it through a single linear layer\n",
        "                state_size = subspace.shape[0]\n",
        "                extractors[\"state\"] = nn.Linear(state_size, 64)\n",
        "                total_concat_size += 64\n",
        "\n",
        "        self.extractors = nn.ModuleDict(extractors)\n",
        "        self._features_dim = total_concat_size\n",
        "\n",
        "    def forward(self, observations) -> th.Tensor:\n",
        "        encoded_tensor_list = []\n",
        "        # self.extractors contain nn.Modules that do all the processing.\n",
        "        for key, extractor in self.extractors.items():\n",
        "            if key == \"rgbd\":\n",
        "                observations[key] = observations[key].permute((0, 3, 1, 2))\n",
        "            encoded_tensor_list.append(extractor(observations[key]))\n",
        "        # Return a (B, self._features_dim) PyTorch tensor, where B is batch dimension.\n",
        "        return th.cat(encoded_tensor_list, dim=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iubRq3ui8Vr1"
      },
      "source": [
        "We have a custom model / feature extractor ready, an observation wrapper that allows SB3 to work with ManiSkill environments, all that is left now is to setup an RL agent and train, evaluate, and monitor it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioewgi6rPhNa"
      },
      "source": [
        "### 2.4 Setting up Training and Evaluation environments\n",
        "\n",
        "First, we will create vectorized environments to speed up training by using the `make_vec_env` function from earlier. We will also make evaluation environments to record evaluation videos during and after training.\n",
        "\n",
        "Additionally, note that we also use the `SB3VecEnvWrapper` wrapper which we provide. This is to format the Maniskill `VecEnv` into a SB3 compatible one so SB3 wrappers like `VecMonitor` can also work, too.\n",
        "\n",
        "Note that currently Maniskill `VecEnv` does not support rendering with `env.render`. So we will need to create a `make_env` function that uses `gym.make` to create environments as well for evaluation purposes and recording videos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2glrHcXoX3c",
        "outputId": "9ad38a8e-d352-4aca-a5b8-ad1787414482"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "\u001b[32m2023-08-18 00:57:15,919 - mani_skill2 - INFO - RenderServer is running at: localhost:44947\u001b[0m\n",
            "/usr/local/lib/python3.10/dist-packages/stable_baselines3/common/vec_env/base_vec_env.py:74: UserWarning: The `render_mode` attribute is not defined in your environment. It will be set to None.\n",
            "  warnings.warn(\"The `render_mode` attribute is not defined in your environment. It will be set to None.\")\n"
          ]
        }
      ],
      "source": [
        "from mani_skill2.vector.wrappers.sb3 import SB3VecEnvWrapper\n",
        "from mani_skill2.utils.wrappers import RecordEpisode\n",
        "from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor\n",
        "from stable_baselines3.common.utils import set_random_seed\n",
        "from functools import partial\n",
        "\n",
        "num_envs = 2 # you can increases this if you have more cores to speed up training\n",
        "env_id = \"LiftCube-v0\"\n",
        "obs_mode = \"rgbd\"\n",
        "control_mode = \"pd_ee_delta_pose\"\n",
        "reward_mode = \"normalized_dense\" # this the default reward mode which is a dense reward scaled to [0, 1]\n",
        "\n",
        "# define a make_env function for Stable Baselines\n",
        "def make_env(env_id: str, max_episode_steps=None, record_dir: str = None):\n",
        "    # NOTE: Import envs here so that they are registered with gym in subprocesses\n",
        "    import mani_skill2.envs\n",
        "\n",
        "    env = gym.make(env_id, obs_mode=obs_mode, control_mode=control_mode, reward_mode=reward_mode, max_episode_steps=max_episode_steps, render_mode=\"cameras\")\n",
        "    # For training, we regard the task as a continuous task with infinite horizon.\n",
        "    # you can use the ContinuousTaskWrapper here for that\n",
        "    if max_episode_steps is not None:\n",
        "        env = ContinuousTaskWrapper(env)\n",
        "    env = ManiSkillRGBDWrapper(env)\n",
        "    # For evaluation, we record videos\n",
        "    if record_dir is not None:\n",
        "        env = SuccessInfoWrapper(env)\n",
        "        env = RecordEpisode(env, record_dir, save_trajectory=False, info_on_video=True)\n",
        "    return env\n",
        "\n",
        "# create one eval environment. Partial must be used here\n",
        "env_fn = partial(\n",
        "    make_env,\n",
        "    env_id,\n",
        "    record_dir=\"logs/videos\",\n",
        ")\n",
        "eval_env = SubprocVecEnv([env_fn for i in range(1)])\n",
        "eval_env = VecMonitor(eval_env) # attach this so SB3 can log reward metrics\n",
        "eval_env.seed(0)\n",
        "eval_env.reset()\n",
        "\n",
        "# create num_envs training environments, with max_episode_steps=50\n",
        "# instead of the default 200 to speed up training\n",
        "env: VecEnv = make_vec_env(\n",
        "    env_id,\n",
        "    num_envs,\n",
        "    obs_mode=obs_mode,\n",
        "    reward_mode=reward_mode,\n",
        "    control_mode=control_mode,\n",
        "    max_episode_steps=50,\n",
        "    # specify wrappers for each individual environment e.g here we specify the\n",
        "    # Continuous task wrapper and pass in the max_episode_steps parameter via the partial tool\n",
        "    wrappers=[\n",
        "        ContinuousTaskWrapper\n",
        "    ]\n",
        ")\n",
        "env = ManiSkillRGBDVecEnvWrapper(env)\n",
        "# use the maniskill provided SB3VecEnvWrapper to make the environment compatible with SB3\n",
        "env = SB3VecEnvWrapper(env)\n",
        "env = VecMonitor(env)\n",
        "env.seed(0)\n",
        "obs = env.reset()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGO65RDTo9ba"
      },
      "source": [
        "To help monitor our training, we can create an evaluation callback function as well as a checkpoint callback function using SB3. The evaluation callback will periodically evaluate the agent without noise and save results to the logs folder. The `eval_env` will also save videos to `logs/videos`. Whenever the evaluation reward has improved, it will save a new best model as well. Finally, the checkpoint callback will periodically save the training progress over time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "U4boL-jVo6up"
      },
      "outputs": [],
      "source": [
        "from stable_baselines3.common.callbacks import EvalCallback, CheckpointCallback\n",
        "\n",
        "eval_callback = EvalCallback(eval_env, best_model_save_path=\"./logs/\",\n",
        "                         log_path=\"./logs/\", eval_freq=32000,\n",
        "                         deterministic=True, render=False)\n",
        "checkpoint_callback = CheckpointCallback(\n",
        "    save_freq=32000,\n",
        "    save_path=\"./logs/\",\n",
        "    name_prefix=\"rl_model\",\n",
        "    save_replay_buffer=True,\n",
        "    save_vecnormalize=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKDwLPiAvEoE"
      },
      "source": [
        "### 2.5 RL Training with PPO\n",
        "Finally, we can begin training. We have to first define the policy and training configuration. The configs provided are already tuned and will be able to train out a succesful LiftCube policy. The configs are set to perform a rollout of 3200 steps split across each parallel environment, and update the policy with batch size 400 for 5 epochs. All logs, including tensorboard logs (run `tensorboard --logdir logs` to vie wthem) and evaluation videos are stored in the `logs` folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcPfMGLj1U4L",
        "outputId": "be3d7c96-4ac3-4714-ff0e-ad6c5e3de66f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cuda device\n"
          ]
        }
      ],
      "source": [
        "from stable_baselines3 import PPO\n",
        "\n",
        "set_random_seed(0) # set SB3's global seed to 0\n",
        "rollout_steps = 3200\n",
        "\n",
        "# create our model\n",
        "policy_kwargs = dict(features_extractor_class=CustomExtractor, net_arch=[256, 128])\n",
        "model = PPO(\"MultiInputPolicy\", env, policy_kwargs=policy_kwargs, verbose=1,\n",
        "    n_steps=rollout_steps // num_envs, batch_size=400,\n",
        "    n_epochs=5,\n",
        "    tensorboard_log=\"./logs\",\n",
        "    gamma=0.8,\n",
        "    target_kl=0.2\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8VNtXkq51tvS"
      },
      "source": [
        "Run the next cell to train the model for 160,000+ steps. The final model is then saved to `logs/latest_model`. This can take 15 min to 1 hour (depending on your machine) to finish training. To speed up training you can use a computer with more CPU cores and/or a more powerful GPU.\n",
        "\n",
        "To keep track of training progress you can go to `logs/videos` and download the evaluation videos saved during training.\n",
        "\n",
        "We have also provided weights trained through this colab tutorial so if you wish to skip the training time  you can skip the next cell and run the following one"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sai8lHHcvG5J"
      },
      "outputs": [],
      "source": [
        "# Train with PPO\n",
        "model.learn(128_000, callback=[checkpoint_callback, eval_callback])\n",
        "model.save(\"./logs/latest_model\")\n",
        "\n",
        "# optionally load back the model that was saved\n",
        "model = model.load(\"./logs/latest_model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "dN0BinYA1Kcn"
      },
      "outputs": [],
      "source": [
        "# Code for simply loading a pretrained policy\n",
        "import urllib.request\n",
        "urllib.request.urlretrieve(\n",
        "    \"https://huggingface.co/datasets/haosulab/ManiSkill2/resolve/main/pretrained_models/tutorials/LiftCube-v0_rl.rgbd.pd_ee_delta_pose.zip\",\n",
        "    \"pretrained_model.zip\")\n",
        "model.policy = model.load(\"pretrained_model\").policy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q4DkwBoZRSpu"
      },
      "source": [
        "### 2.6 Evaluation\n",
        "Once a model is trained, whether you ran the script above or downloaded the pretrained policy, you can run below to evaluate it and save some videos. You can set `render=True` if you have a GUI to create to live render the evaluation.\n",
        "\n",
        "If you use default configurations / use the pretrained model, you should get around 60%+ success rate on LiftCube. If you train longer you can get 90%+ success rate.\n",
        "\n",
        "**Note that the green sphere in the videos represents the target height to lift the cube to (not a goal position).**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jTdNiAmSZ6xF",
        "outputId": "19b7ad90-c877-439d-892c-1f9991364bf9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Success Rate: 0.6\n",
            "Episode Lengths: [33, 36, 200, 58, 200]\n"
          ]
        }
      ],
      "source": [
        "from stable_baselines3.common.evaluation import evaluate_policy\n",
        "eval_env.close() # close the old eval env\n",
        "# make a new one that saves to a different directory\n",
        "env_fn = partial(\n",
        "    make_env,\n",
        "    env_id,\n",
        "    record_dir=\"logs/eval_videos\",\n",
        ")\n",
        "eval_env = SubprocVecEnv([env_fn for i in range(1)])\n",
        "eval_env = VecMonitor(eval_env) # attach this so SB3 can log reward metrics\n",
        "eval_env.seed(0)\n",
        "eval_env.reset()\n",
        "\n",
        "returns, ep_lens = evaluate_policy(model, eval_env, deterministic=True, render=False, return_episode_rewards=True, n_eval_episodes=5)\n",
        "success = np.array(ep_lens) < 200 # episode length < 200 means we solved the task before time ran out\n",
        "success_rate = success.mean()\n",
        "print(f\"Success Rate: {success_rate}\")\n",
        "print(f\"Episode Lengths: {ep_lens}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "K9l6AjvCdGjj",
        "outputId": "1c1c993a-ca4c-4082-9521-b90eaf06f6a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Video object>"
            ],
            "text/html": [
              "<video controls  >\n",
              " <source 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\" type=\"video/mp4\">\n",
              " Your browser does not support the video tag.\n",
              " </video>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "from IPython.display import Video\n",
        "Video(\"./logs/eval_videos/0.mp4\", embed=True) # Watch one of the replays"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4X-prXDwyLEF"
      },
      "source": [
        "## 3 Final Thoughts\n",
        "\n",
        "This tutorial demonstrates a simple approach to solving ManiSkill2 environments with state-based and visual-based Reinforcement Learning (RL). While LiftCube may appear solved already, other environments are much more complex and solving them scalably is an open problem!\n",
        "\n",
        "Imitation Learning (IL) is another technique which leverages the demonstration dataset of ManiSkill2 to help learn complex skills. For a tutorial on Imitation Learning with ManiSkill2, see our [IL Colab Tutorial](https://colab.research.google.com/github/haosulab/ManiSkill2/blob/tutorials/examples/tutorials/3_imitation_learning.ipynb)\n",
        "\n",
        "While our environments and code enable much faster visual-based RL and IL compared to other robotics environments, there are still a number of approaches that can enhance visual-based policies. Many of these approaches have been consolidated into our own library called [ManiSkill2-Learn](https://github.com/haosulab/ManiSkill2-Learn) which has code to leverage RGBD and PointClouds, transformers, and more.\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "1T7S6wmryEub"
      ],
      "provenance": [],
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "mani_skill2",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.11.1"
    },
    "vscode": {
      "interpreter": {
        "hash": "27bbf14ba245321bda1133d220a06c17414b7fdf9e1ceccf1e40995cf2d3671b"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}